key: cord-0000427-zueoyesj
authors: Berretta, Regina; Moscato, Pablo
title: Cancer Biomarker Discovery: The Entropic Hallmark
date: 2010-08-18
journal: PLoS One
DOI: 10.1371/journal.pone.0012262
sha: ab371ed38caa53c720fd4c0333e1bf4de84550f1
doc_id: 427
cord_uid: zueoyesj

BACKGROUND: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. METHODOLOGY/PRINCIPAL FINDINGS: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. CONCLUSIONS/SIGNIFICANCE: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-througput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.

In a seminal review paper published nine years ago, Hanahan and Weinberg [1] introduced the ''hallmarks of cancer''. They are six essential alterations of cell physiology that generally occur in cancer cells independently of the originating tissue type. They listed: ''self-sufficiency in growth signals, insensitivity to growth-inhibitory signals, evasion of the normal programmed-cell mechanisms (apoptosis), limitless replicative potential, sustained angiogenesis, and finally, tissue invasion and metastasis''. More recently, several researchers have advocated including ''stemness'' as the seventh hallmark of cancer cells. This conclusion has been reached from the outcomes of the analysis of high-throughput gene expression datasets [2, 3] . The new role of stemness as a hallmark change of cancer cells is also supported by the observation that histologically poorly differentiated tumors show transcriptional profiles on which there is an overexpression of genes normally enriched in embryonic stem cells. For example, in breast cancer the activation targets of the pluripotency markers like NANOG, OCT4, SOX2 and c-MYC have been shown to be overexpressed in poorly differentiated tumors in marked contrast with their expression in welldifferentiated tumors [4] .

Other authors suggest different hallmarks, with many papers pointing alternative processes as their primary focus of their research. The difference may stem from the fact that these authors prefer to cite as ''key hallmarks'' physiological changes which occur at a ''lower level'' scale closer to the molecular events. These authors cite, for example, ''mitochondrial dysfunction'' [5, 6] (including, but not limited to ''glucose avidity'' [7] and ''a shift in glucosemetabolism from oxidative phosphorylation to glycolysis'' [6, 8] , ''altered glycolysis'' [9] , ''altered bioenergetic function of mitochondria'' [10] ), ''dysregulation of cell cycle and defective genome-integrity checkpoints'' [11] , ''aberrant DNA methylation'' [12] (''promoter hypermethylation of hallmark cancer genes'' [13] and ''CpG island hypermethylation and global genomic hypomethylation'' [14] ), ''shift in cellular metabolism'' [15, 16, 17] , ''regional hypoxia'' [18] , ''microenviroment acidosis'' [19] , ''abnormal microRNA regulation'' [20, 21] , ''aneuploidy'' and ''chromosome aberrations'' [22, 23, 24, 25, 26] , ''disruption of cellular junctions'' [27] , ''avoidance of the immune response'' [28] , ''pre-existing chronic inflammatory conditions'' [29, 30] , ''cancerrelated inflammation'' [29] , ''disabled autophagy'' [28] , ''impaired cellular senescence'' [31] , ''altered NF-kappaB signalling'' [32] , ''altered growth patterns, not altered growth per se'' [33] , ''disregulated DNA methylation and histone modifications'' [34] , ''tissue dedifferentiation'' [35, 36] , and ''somatically heritable molecular alterations'' [37] . This research enriches the list of the most important cancer hallmarks. However, these physiological changes occur at a ''lower'' molecular level they are likely related sub events of the orginial seven instead of newly discovered ''key hallmarks''. More recently, Luo et al attempted a ''stress-based'' description of some of the hallmarks in terms of ''stresses'' (''DNA damage/replication stress, proteotoxic stress, mitotic stress, metabolic stress, and oxidative stress'') [38] . While this is an interesting descriptive grouping, it is still a phenotypical characterization. What is needed is a higher level unifying genotypical characterization, from which individual disregulated processes can be identified in a quantitative way using the existing high-throughput data capture methodologies. It is clear that a unifying hallmark is needed if we aim at quantifying the cell's progression. It is then evident for us that a unifying mathematical formalism is necessary to uncover the cell transcriptome's progression from a normal to a more malignant phenotype.

We start our quest assuming an implicit working hypothesis common to many research groups around the world: the macroscopic physiological changes (i.e. Hanahan and Weinberg's ''hallmarks'') must also correlate with global alterations of the molecular profiles of gene transcription. It is also assumed that the ''hallmark changes'' occur along a certain timeline, but that some of the sub-processes discussed before are concurrent. These processes may start in a slow incremental way with some of the major changes being early events while others (e.g. tissue invasion and metastasis) are likely later processes triggered by new events during cancer progression. The timeline is not explicit and it is also likely that cancer subtypes progress to similar timelines. In some cases the sequence of events are better understood (e.g. some leukaemia subtypes [39] ). The elicitation and regulation of molecular events is likely to be an ongoing quest during this century for many types of cancer.

It is not to be assumed that some of the transitions of the transcriptome are gradual. That is a hypothesis that is unnecessary in this study. We envision that the progression of cancer may have ''switches'', with a number of concurrent converging events leading to macroscopic observable changes in the gene expression profile resulting in dramatic variations of expression patterns. For instance, these molecular switches could not be characterized by an ''oncogene'' but by a large number of the genes that have changed its transcriptional state. These abrupt changes may be triggered by the confluence of several non-linear interactions, and are likely to be related to the physiological hallmarks we refer to above.

The presence of macroscopic observable changes that are computable from a large number of relatively smaller changes mean that it may be possible to find an objective mathematical formalism to infer the turning point at which these radical changes occur.

It is then evident that computing the Jensen-Shannon divergences, the Normalized Shannon Entropy, and the Statistical Complexity of samples reveal different global transcriptional changes. It is, however, not easy to infer if these changes would correlate with a gradual progression or sudden changes. However, one valid mathematical possibility is that the most important ''hallmark of cancer'', a unifying principle above all, is the existence of a measurable gradual ''progression'' from a well-differentiated gene expression profile (corresponding to a healthy tissue). This would reveal the timeline of a higher level process that is observable and measurable via a change of Normalized Shannon Entropy and an increment of Jensen-Shannon divergences from the originating tissue type. If this is the case, by correlating the changes in Information Theory quantifiers with the expression of the genes we would be able to not only uncover useful biomarkers to track this progression but to explain the ''hallmarks'' in an ordered timeline. The timeline also yields clinical and translational important outcomes. Such analytical methodology will naturally produce ''a continuous staging'' of the cancer samples, based on a solid foundations of Information Theory, based on the knowledge of transcriptional profile of healthy cells as reference to measure divergences. In addition, as a mathematical methodology, it can be applied to other high-throughput technologies for which a probability distribution function of observed abundances has been computed.

With these ideas in mind, we provide a ''transcriptomic-driven'' method revealing important biomarkers for cancer progression a direction of time for which they are presented. The method, however, is generalizable to other type of high-throughtput techonologies (e.g. proteomic studies). We have chosen two types of cancers to study which are almost at the antipodes in terms of progression rates: prostate cancer and melanoma.

Prostate cancer progresses very slowly. Pathological samples are common in autopsies of men as young as 20 years old. By the age of 70 more than 80% of men have these alterations, a fact that already shows a relationship of this cancer type with increasing age. The clinical management of prostate cancer requires the identification of the so-called Gleason patterns in the biopsies [40] , which after almost fifty years is still ''the sole prostatic carcinoma grading system recommended by the World Health Organization''. However, undergrading, underdiagnosis, interobserver reproducibility and variable trends in grading have been observed as major problems [41, 42] . Melanoma, on the other hand, differs from prostate cancer in its rapid progression [43] and it is considered one of the most aggressive types of cancer. One of melanoma's usual markers of progression and concern (i.e thickness) is measured in millimetres, which gives a rough idea of how devastatingly fast the disease can spread.

We will present our results starting with one prostate cancer dataset, followed by another in melanoma, to come back to the prostate cancer discussion using another highly relevant dataset. This is a departure from the alternative approach in which each disease is discussed in separate sections. However, after considering several possibilities, we are convinced that our approach is the most appropriate to showcase the technique and its power. Details on the datasets and methods used are given in the 'Materials and Methods' section of this paper. We also refer to the original studies and manuscripts associated to the three datasets we analysed. and available at the web address given above). After imputation of missing values, we first calculated the Normalized Shannon Entropy and the MPR-Statistical Complexity for the each sample.

The flowing section explains the context in which our results were generated (refer to the 'Materials and Methods' section for detail on how our quantities are computed). The Normalized Shannon Entropy measure is widely used in ecosystem modelling to quantify species diversity, where it is acknowledge as having great sensitivity to relative abundances of species in an ecosystem [45] . We utilise the same sensitivity to differentiate a samples in cancer datasets. Figure 1 shows that the Normalized Shannon Entropy of prostate cancer tumor samples do not differ much from normal samples. This is in contrast to lymph node metastasis samples that appear to have smaller values of Normalized Shannon Entropy.

A mathematical interpretation of this result is that the samples from lymph node metastases have cells that not only varied their transcriptomic profile, they have also ''peaked'' the distribution of expression values with significant fold increases on a smaller number of probes. This explains the reduction in Normalized Shannon Entropy. We note that there are several mechanisms that can explain a macroscopically observable global reduction of transcription. For instance, this may indicate that a relatively large number of genes have reduced their expression levels by genome damage, changes in gene regulation, or other silencing processes. It is reassuring to observe that the changes of the most prototypical quantitative measure we can draw from Information Theory, the Normalized Shannon Entropy correlate well with the transition between normal samples with to ones with metastases. However, it is also evident from that normal samples do not differentiate much from the tumor group (the Normalized Shannon Entropy values do not differ much). It is then not the number of genes with high expression values, but the change in the distribution of expression levels on the molecular profile, that can provide the other measure that could distinguish these other samples. This must be handled by the other statistical complexity measures to be discussed next.

Several statistical complexity measures can be defined which aim to clarify our argument. We will first discuss the results of computing the MPR-Statistical Complexity measure (in the previous figure the y-coordinates correspond to the MPR-Statistical Complexity values of each sample). The MPR-Statistical Complexity is proportional to both the Normalized Shannon Entropy associated to the transcription profile and the Jensen-Shannon's divergence between that probability density function and the uniform probability distribution. Again, we refer the reader to the 'Materials and Methods' section for an explanation of how these magnitudes are computed.

Although the results of using the MPR-Statistical Complexity might not seem particularly impressive, there are a few reasons why we introduce them at this stage. We want to illustrate a fact that can already be observed when we employ this measure on this dataset. In this dataset, for a given entropy value interval, normal tissue samples tend to have relatively lower MPR-Statistical Complexity values than tumor and lymph node metastasis. This means that both prostate cancer and metastases samples diverge from a ''more uniform'' distribution indicating that the distribution ''peaks'' in fewer active genes. It also means that, in terms of Jensen-Shannon's divergence, the transcriptional profile of a normal prostate cell sample is ''closer'' to a uniform distribution than to the one that is observed in a prostate cancer cell sample.

The reader will readily argue, and with reason, that the transcriptional profile of a normal cell is tissue-specific and that it hardly resembles that of a uniform distribution of expression values. That is correct and this observation motivates the introduction of two new statistical complexity measures. We generically call these two variants as 'M-complexities' (with 'M' standing for ''modified''). They have the same functional form as the MPR-Statistical Complexity, but instead of computing the Jensen-Shannon's divergence from a uniform probability distribution we compute it against an ad hoc probability distribution functions derived from the data. In this sense, these measures are more supervised then the MPR-Statistical Complexity is. Another perspective is that the MPR-Statistical Complexity is a special case of this measure in which the ad hoc probability distribution function of reference is the equiprobability distribution. The relevance of this measure derives from being a general definition that allows [44] . Metastatic samples have typically lower values of Normalized Shannon Entropy than normal samples and prostate cancer primary tumors. The reduction in Normalized Shannon Entropy indicates that there exists a significant reduction on the expression of a large number of genes, or that the gene profile of metastatic samples has a more ''peaked'' distribution (due to the upregulation of a selected subset of genes). Both possibilities just cited are not mutually exclusive. We also note that neither the Normalized Shannon Entropy, nor the MPR-Statistical Complexity (as a single unsupervised quantifier), can help differentiate between tumor and normal samples, indicating that other Information Theory quantifiers are required for this discrimination. doi:10.1371/journal.pone.0012262.g001 accommodating several different reference states. We will use it to measure divergences to the ''initial'' and ''final'' transcriptomic states (two states of reference). Taken as computed averages over normal samples, and respectively metastatic ones, these measures will allow tracking the processes of differentiation of a cancer cell from a particular tissue type.

For example, using Lapointe et al.'s dataset, the M-Normal statistical complexity quantifier first requires the computation of the probability distribution function of the average gene expression profile of all normal prostate samples. Afterwards, the Normalized Shannon Entropy and the Jensen-Shannon's divergence of any sample profile will be computed using the divergence to that averaged normal distribution. Analogously, we compute the M-Metastases statistical complexity quantifier by first calculating the average profile of the metastases samples, and then generating the corresponding probability distribution function, finally computing the Jensen-Shannon's divergence with that profile. We refer to the 'Materials and Methods' section for details of the calculations.

The results can be observed in Figure 2 . On the x-axis, the lymph node metastases have the largest values of M-Normal indicating a divergence from the normal profile. In addition, the M-metastases values of normal samples tend to be higher than most of the metastasis samples (with the exception of only one). Figure 2 shows a gradual progression of the samples positions on this plane from a well-differentiated tissue type specific profile, first to a more heterogeneous primary tumor cluster, and finally to an even less differentiated metastatic profile.

The result presented in Figure 2 shows that the prostate cancer samples, which are not metastases and therefore could have been scattered anywhere on the plane, are clustered on a particular confined area between the two other groups. We understand that there are reasons to be sceptical about this result being not just a simple consequence of the gene selection process used by Lapointe et al. For example, if we assume that the 5,153 probes singled out by Lapointe et al. in their figure one of Ref. [44] (and that constitute our original data) have been selected with a supervised method that try to distinguish between normal and metastases, then the relative position of normal and metastases samples is perhaps something to be expected. However, even under that assumption, what is not expected is the position of all primary tumor prostate cancer samples, linking the normal cluster of samples with the metastases one. Note that the definition of both the M-Normal and M-Metastases measures do not use any information from the primary tumor prostate cancer samples, so the location of these samples between the normal cluster and the metastases, bridging them naturally is something to highlight. Together with Figure 1 , it gives evidence that supports the working hypothesis that a gradual ''progression'' occurs, from the normal tissue specific profile to the metastasis one.

Indeed, following our line of argument, Figure 2 has even more relevance when we highlight the fact that the 5,153 probes have not been selected with a supervised method. The authors say that the only selection criteria was to single out the 5,153 cDNAs whose expression varied most across samples. In the supplementary notes of their paper the authors say: ''We included for subsequent analysis only well measured genes whose expression varied, as determined by (1) signal intensity over background .1.5-fold in both test and reference channels in at least 75% of samples, and (2) 3-fold ratio variation from the mean in at least two samples; 5,153 genes met these criteria.'' As a consequence, Figure 2 has been generated without class selection bias only using the genes that have the most varied expression pattern.

We now turn to another aspect of the statistical complexity and entropy analysis. We note that Figure 2 shows that the metastases samples have a clear reduction on Normalized Shannon Entropy in comparison with the values observed for the normal samples. At the same time, metastases samples, as expected, have higher Mnormal complexity than the normal samples ( Figure 2 ). It is then interesting to evaluate the value of the Jensen-Shannon divergence of these samples and to identify the genes that most correlate with the variations of Jensen-Shannon divergence to quantify one of the factors that is related to the statistical complexity changes.

We have computed the correlation of the gene expression profile corresponding to each of the 5,123 probes. For each of the 5,123 probes, we computed both the Pearson correlation (x-axis of Figure 3 ) and the Spearman correlation (y-axis of Figure 3 ) of each probe profile with the Jensen-Shannon divergence having as probability distribution of reference that of a metastasis profile (these values are called JSM2-Pearson and JSM2-Spearman in the accompanying Excel file provided). With this data, we have produced Figure 3 , a scatter plot of the values associated to each probe. In this figure, there are two probes that are immediately recognizable by any cancer researcher, and in particular for those in prostate cancer: KLK3/PSA (Prostate Specific Antigen) and FOS.

The interpretation of these scatter plots is not immediate and needs an introductory explanation. Each dot corresponds to one probe of the array. For example, a dot that is very close to the origin of coordinates (0,0) indicates a probe such that its pattern of gene expression (across all samples) is not correlated with the Jensen-Shannon divergence to the average profile of a metastasis pattern. It is, in essence, a probe which is highly uninteresting in this regard. Probes that have a high correlation, across all samples, either positive or negative with the Jensen-Shannon divergence to the average profile of a metastasis pattern are highly informative. They ''co-express'' with this measure.

Although we provide in the supplementary material the information corresponding to all probes, we will discuss just a few of them. This will allow the reader to understand these plots and will put our results in the perspective with current research in prostate cancer. We particularly highlight the position of KLK3/ PSA, FOS and CCL2. To our surprise, we have found which is perhaps the most famous biomarker in prostate cancer KLK3/ PSA (Kallikrein-related peptidase 3), probe G_914588 (correlations of 20.9312 and 20.9000 respectively). FOS and KLK3/ PSA are the second and the fourth most negatively correlated probes in this ranking of all the genes in the microarray. With opposite signs for correlations are CDKN2D, FOXM1, and BRCA2. The following is a discussion of a selection of probes (highlighted in Figure 3 ) in the context of prostate cancer.

CDKN2D (Cyclin-dependent kinase inhibitor 2D, p19, inhibits CDK4). One of the genes that has strong positive correlations is CDKN2D, (Cyclin-dependent kinase inhibitor 2D, p19, inhibits CDK4) (Pearson correlation of 0.7543, Spearman correlation 0.6833), probe G_145503. A gene that shows a positive correlation with the divergence of a metastasis profile indicates a gene that has a putative reduced expression on these samples. CDKN2D is a known regulator of cell growth regulator and controls cell cycle G1 progression [46, 47] . Loss of CDKN2D in cancer cells is one event which is generally associated to a more malignant phenotype.

FOXM1. Another probe that presents positive correlations is FOXM1 (Forkhead box M1), with Pearson correlation of 0.7039 and Spearman correlation 0.7500), probe G_564803. It has been recently shown that the depletion of FOXM1 still allows cells to enter mitosis but they are unable to complete cell division. As a consequence this leads to mitotic catastrophe or endoreduplication [48] . FOXM1 is considered a key regulator of a transcriptional cluster which is that is essential for proper execution of the mitotic program and the control of chromosomal stability [49] .

BRCA2 -(Breast cancer 2, early onset). Another gene with positive correlations is BRCA2 (Breast cancer 2, early onset), probe G_193736, with Pearson correlation of 0.8161 and Spearman correlation 0.7333). While the loss of BRCA2 function and its consequences in prostate cancer is being reconsidered [50, 51, 52, 53] , BRCA2 is generally regarded as a ''tumor suppressor'', with an established role in maintaining genomic stability via its function in the homologous recombination pathway for double-strand DNA repair. This result is supporting its proposed function. Loss of BRCA2 function is thus a warning sign of the existence of error prone cell processes. In prostate cancer BRCA2 has been associated to promotion of invasion through upregulation of MMP9 [54] . BRCA2 loss of function due to mutations is linked to poor survival in prostate cancer [55] and rare germline mutations have been associated with early-onset of prostate cancer [56] .

CCL2/MCP-1 (chemokine (C-C motif) ligand 2). Bone is one of the most common sites of prostate cancer metastasis; close to 85% of men who die of prostate cancer have bone metastasis [57] . The successful metastatic process to bone follows from the activation of osteoclasts with bone resorption, which in turns leads to the release of different growth factors from the bone matrix [58] . CCL2 has been previously reported as expressed in human bone marrow endothelial cells; the CCL2 stimulation promotes prostate cancer cell migration and proliferation [57, 59] and it has been proposed as a paracrine and autocrine factor for invasion and growth of prostate cancer [60] . As a consequence of this central role in the tumor microenvironment, CCL2 is being the object of several studies and is included in the list of potential targets for novel therapies [60, 61, 62, 63, 64, 65, 66, 67, 68, 69] .

FOS (V-fos FBJ murine osteosarcoma viral oncogene homolog). A probe for FOS (G_811015; correlations of 20.9380 and 20.9500 computed with Pearson and Spearman) has a similar correlation than KLK3/PSA. The high rank of FOS was unexpected, but perhaps it is less of a surprise for some experienced researchers in prostate cancer as its role has been highlighted in the past [70, 71, 72] . Amplification of members of the MAPK pathway was associated with androgen independent prostate cancer, and co-expression of RAF1, ERBB2/HER2 and c-FOS would lead to this phenotype [73] .

We will not discuss in depth the known relationships between FOS, Lamin A/C and prostate cancer. We leave this discussion for later, as Lamin A/C will also appear in our study of the other prostate cancer dataset studied in this paper. Lamin A/C appears as a member of a set of genes with reduced expression for higher grade primary prostate cancer samples (note that the current analysis that gave FOS as a biomarker is on lymph node metastatic samples like here). However, we would like to point out a connection that is currently hypothesized between Lamin A/C and FOS, the gene we have just discussed. Ivorra et al. have recently proposed that ''lamin A overexpression causes growth arrest, and ectopic c-Fos partially overcomes lamin A/C-induced cell cycle alterations. We propose lamin A/C-mediated c-Fos sequestration at the nuclear envelope as a novel mechanism of transcriptional and cell cycle control'' [74] . In addition: ''c-Fos accumulation within the extraction-resistant nuclear fraction (ERNF) and its interaction with lamin A are reduced and enhanced by gain-of and lossof ERK1/2 activity, respectively.'' [75] . These novel interactions between LMNA and FOS, their putative role in prostate cancer metastasis and their seemingly different behaviours in prostate cancer lymph node metastases warrant further investigation.

SOX9 (SRY (sex determining region Y)-box 9). This transcription factor has been recently identified as having an importat role during embryogenesis and in the early stages of prostate development [76, 77] and in testis determination [78] , processes that link SOX9 upregulation to cancer development [79] . Basal epithelial cells do express SOX9 in a normal prostate. While there exists no detectable expression in lumina epithelial cells, SOX9 has already been reported as ''expressed in primary prostate cancer in vivo, at a higher frequency in recurrent prostate cancer and in prostate cancer cell lines (LNCaP, CWR22, PC3, and DU145)'' [80] . Wang et al., also in [80] add that: ''Significantly, down-regulation of SOX9 by siRNA in prostate cancer cells reduced endogenous AR protein levels, and cell growth indicating that SOX9 contributes to AR regulation and decreased cellular proliferation. These results indicate that SOX9 in prostate basal cells supports the development and maintenance of the luminal epithelium and that a subset of prostate cancer cells may escape basal cell requirements through SOX9 expression.'' An increased value of SOX9 expression in advanced prostate cancer has been associated to tumor progression and the epithelial-mesenchymal transition [81] . SOX9 expression has been associated with a putative subgroup of prostate cancer [82] , associated to lymph-node metastasis (as seems to be the case in this dataset) and has a know role in chondrogenic differentiation processes [83] .

KLK3/PSA -(Kallikrein-related peptidase 3)/Prostate Specific Antigen. To finalize our initial discussion on this dataset, we address KLK3. The high ranking of KLK3/PSA in our list is perhaps one of the most remarkable retrodictive outcomes of our approach. KLK3/PSA (also known as Prostate Specific Antigen) is a conspiquous member of our top rank list. It is perhaps the best blood biomarker for prostate cancer screening. Its relevance and popularity as a target of studies is so wide that it makes unfeasible any serious attempt to uncover its relevance in the prostate cancer literature. A search using PubMed using the keyword 'KLK3' (and the other alias names of this gene) reveals a total of 11,429 published papers. Of course, many of these publications relate to its role for early screening, but in this study we are uncovering its role as a tissue biomarker. Our results echoes a recent contribution by S. Miyano's and his collaborators [84] on a massive meta-analysis of microarray datasets. It is also in line with results from clinical studies that indicate that a 5-year PSA value is useful for predicting prostate cancer recurrence. [85] . Certainly the dynamics of PSA, now perhaps with FOS and SOX9 added to the set of biomarkers of interest, warrant further investigation for patient population stratification after initial treatment.

The biomarkers discussed in this section warrant further investigation in prediction of lymph-node metastasis and clinical management of prostate cancer [86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109] . We refer the reader to the Supplementary Material to have a complete list of probes and their correlations with the Information Theory quantifiers.

The following sections present the results that we obtained with a melanoma dataset. Our aim is to observe if variations of the Normalized Shannon Entropy and the statistical complexity measures, MPR-complexity and the modified forms M-normal and M-metastases, provide interesting results in a different disease and experimental setting.

In this case we have selected a gene expression dataset from Haqq et al. [110] containing information of 14,772 cDNAs in 37 samples (Figure two from the [110] ). The 37 samples include 3 normal skin, 9 nevi, 6 primary melanoma and 19 melanoma metastases. This datasets has more phenotypical characteristics for the group of samples.

After an initial process of data cleaning, we removed 35 probes which had an unsually high expression value on only a few samples, in some cases on a single one. The dataset we work with from original contributed by Haqq et al.consists of 14,737 probes. First, we computed the Normalized Shannon Entropy and the MPR-Statistical Complexity for each sample (refer to the 'Materials and Methods' section for a detailed presentation of these calculations). Figure 4 shows the values of these quantifiers for each sample.

We first observe an important difference between Figure 1 and Figure 4 . In this melanoma dataset, neither the use of the Normalized Shannon Entropy nor the MPR-complexity helps to discriminate between normal skin, nevi, primary and metastastic melanomas. Nevertheless, we decided to present this figure for methodological reasons. We envision that some researchers will calculate the Normalized Shannon Entropy and MPR-complexity using all the probes. We note that in Figure one of Haqq et al's original paper, the whole probe set was previously filtered by selecting those which vary across samples, thus indicating that they may have information about disease subtypes (although the phenotypic types were not biasing the selection). In this case we want to illustrate both the Normalized Shannon Entropy and MPR-complexity calculated using all the probes does not give the expected benefits. We will now see the benefits of using the M-complexities.

As we did for prostate cancer (see Figure 2 ), we aim at identifying if the use of the modified forms of the statistical complexity (the M-complexities) could give some insight where the Normalized Shannon Entropy and MPR-complexity measures fail. To compute the M-normal measure, we need to define the average gene expression profile for a normal cell (which we call P ave ). We thus resort to the three normal skin profiles and we produce the average based on these profiles (details for computing the average profiles are given in the 'Materials and Methods' section). We call M-skin the resulting measure that relies on this profile. Analogously, we need to compute a pattern for M-metastasis, and we proceed to calculate the P ave profile averaging over the 19 metastases samples. The result is encouraging, as samples plotted in the (M-skin, M-metastasis)-plane cluster in groups, showing an important M-skin complexity transition between normal skin cells and nevi. Most importantly, this method naturally shows that some of the metastatic samples have a large value of M-skin complexity, so we present the results of another experiment, aimed at clarifying this fact.

In their original publication, Haqq et al. classified the melanoma metastases in two groups due to their molecular profiles: five samples were classified as 'Type I' and fourteen as 'Type 2' based on a hierarchical clustering approach. Our result reinforced the view that the Type II melanomas metastasis is a pretty homogeneous group, we will present the results on the (Mskin, M-metastasis I)-plane. This means that now the P ave profile will not be obtained by averaging over the 19 metastases samples, but instead using only the 14 samples which have been labelled as 'Type II'. As such, we aim at revealing if Type I samples are indeed different in this plane, and if other clusters are also present. Figure 5 presents the results. The first fact worth commenting is the pronounced gap between normal skin samples and the nevi, primary, and metastatic melanoma samples as revealed by the Mskin measure. Note also that the M-skin is based on the average profile that of the normal samples, which indicates that no information about the profiles of metastasis are used, yet M-skin reveals that increasing values of this measure may be linked with a 'progression' from nevi to primary and metastasis melanoma profiles.

We now introduce another useful technique to identify genes which correlate with the transitions. The challenge is to find genes which are related with the progression towards metastases profiles, even when we recognize that there the group of metastasis samples is heterogeneous (containing at least two groups). Since the final outcome of Figure 4 and Figure 5 is that the Normalized Shannon Entropy does not help much in this experimental scenario, we will concentrate only on one of the multiplicative factors of the Mcomplexities, the Jensen-Shannon divergence. We compute two P ave profiles, one with the normal skin samples only, and the other with all the metastasis samples (regardless their type). We will call the two divergences JSM0 and JSM5 respectively. We then compute the Spearman correlation of the profile of all gene probes in the array across the 37 samples to both JSM0 and JSM5. We have listed all probes according to the absolute value of the difference of these correlations, i.e. Abs. Diff. (probe) = |JSM0(probe)2JSM5

(probe)| in decreasing order. The results are provided as Haqq-PLoSONE-SupFile.xls, in the sheet labelled 'Results-correlation'.

The rationale is to identify those probes which are highly correlated (both positively or negatively) with the Jensen-Shannon divergence of the normal tissue profile and that ''reverse signs''. For instance, a probe for the TP63 gene (Tumor protein p63, keratinocyte transcription factor KET), AA455929, is ranked in the third position. Its correlation with the Jensen-Shannon divergence of the normal skin type is relatively high and negative (JSM0 = 20.63632) while at the same time is has a positive correlation with the Jensen-Shannon divergence of the metastasis profile (JSM5 = 0.62138). In the ranking, the first probe that presents the opposite behaviour is one for ADA (Adenosine deaminase), AA683578. Figure 6 helps to understand the relationship of these correlations with expression. Not only are these genes well correlated with the divergences, they also seem to be good markers of the progression from one tissue type profile to the metastasis profile.

We will now discuss three of these genes in the context of current biological knowledge on melanoma drivers and metastatic progression. We provide many references for one of them, SPP1 (Secreted phosphoprotein 1 or Osteopontin). The discussion on this gene will be left for later, when we will discuss specifc oncosystems related to cell proliferation, chemotaxis and responses to external simulus. Figure 7 shows the expression of ADA (Adenosine deaminase, AA683578) as a function of TP63 (keratinocyte transcription factor KET, AA455929). All normal skin samples, as well as nevi and a couple of primary melanomas have relatively low values of ADA but they express TP63. There is a change of roles in metastatic and some primary melanomas, which have reduced TP63 expression but increased values of expression of ADA. As we will later see, these events correlate with other major transcriptional modifications which involve dozens of genes and that we have been able to map thanks to functional genomics bioinformatics tools. The role of SPP1 will be discussed in that context after some references to TP63, ADA, and PLK1 which follow.

TP63. The product of this gene [111, 112] belongs to the same protein family of its more famous relative, TP53, a gene that is often mutated in human cancers [113] and highly regarded as a key ''tumor suppressor''. TP63's product, p63, is a homologous protein to p53, which is considered to be phylogenetically newer [114] and also regarded as an important apoptotic and cell-cycle arrest protein. Mice that lack TP53 are born alive with a propensity for developing tumours; mice that lack TP63 do not appear to be tumour prone, although, new results are partially contradicting earlier findings [115] . It appears that the diverse roles of the isoforms of the p63 family reveal that there exists a crosstalk with the different isoforms of the p53 family that needs to be systematically investigated [116] . It has recently been shown that p63 is a key regulator of the development of stratified epithelial tissues [113] and that its deletion results in loss of stratified epithelial and of all keratinocytes [117] . Melanocytes also express two isoforms of p63 [118] , but p63 expression is not reported in 57 out of 59 tumors in a tissue microarray study performed by Brinck et al. [119] . It is clear that the the role of loss of expression of TP63 in melanoma warrants further investigation.

ADA -(Adenosine deaminase) and DPP4/CD26 (Dipeptidylpeptidase 4, CD26, adenosine deaminase complexing protein 2). A link between TP63 and ADA has already been reported in the literature. ADA is a gene involved in cell division and proliferatation [120] and it has been suggested to have a regulatory role in dendritic cell innate immune responses [121] .Translational modification is also a function of p63. Sbisa et al. have proved that ADA is a direct target of isoforms of p63, which is an important discovery as ADA has two TP53 binding sites, leading to a complex metabolic balance due to the different relationships between this trio and p21 yet to be completely elicitated [120, 122] . Several studies indicate elevation of adenosine deaminase levels in sera of breast [123] , head and neck [124] , colorectal [125] , acute lymphoblastic leukaemia [126] and laryngeal cancers [127] .

We observe a marked increase of expression of a probe for ADA with melanoma progression while at the same time we observe a loss of expression of a probe corresponding to DPP4/CD26 (Dipeptidylpeptidase 4, CD26, adenosine deaminase complexing protein 2), a membrane-bound, proline-specific serine protease [128] that has been attributed tumor suppressor functions [129] . It has been previously reported that loss of DPP4 immunostaining helps to discriminate malignant melanomas from deep penetrating nevi, a variant of benign melanocytic nevus [130] and early reports of their absence in metastatic melanomas exist [131, 132] . As deep penetrating nevi can mimic the vertical growth phase of nodular malignant melanoma, and ADA could potentially be downregulat-ing DPP4 [133, 134] we believe that the elicitation of the complementary role of these two biomarkers to distinguish these two entities is necessary and also warrants further clinical studies.

PLK1 (Polo-like kinase 1 (Drosophila)). Another probe for gene that ranks high as a positive marker of metastasis is PLK1, Polo-like kinase 1, Serine/Threonine protein kinase 13 (AA629262). PLK1 is a centrosomal kinase [135] which is Figure 7 . Scatter plot showing the expression of the probe corresponding to ADA (Adenosine deaminase), AA683578 (y-axis) and TP63 (Tumor protein p63), AA455929 (x-axis). All the samples that have TP63 expression are normal or nevi, with two primary melanomas still preserving TP63 expression but with higher ADA. The trend reverses for the rest of the primary melanoma samples and the metastatic ones, which all express ADA but not TP63. doi:10.1371/journal.pone.0012262.g007 Analogously, we compute the Jensen-Shannon divergence of each sample with the average metastastic profile and we also compute the correlation of each probe with this measure (y-axis). The position of one probe corresponding to the TP63 gene (Tumor protein p63, keratinocyte transcription factor KET), AA455929, is highlighted. The expression of this probe has a relatively high negative correlation with the Jensen-Shannon divergence of the normal skin type (JSM0-Spearman = 20.63632) while at the same time is has a positive correlation with the Jensen-Shannon divergence of the metastasis profile (JSM5 = 0.62138). The first probe that presents an opposite behaviour is one for ADA (Adenosine deaminase), AA683578. Probes for SPP1 (Secreted phosphoprotein 1 or Osteopontin) and PLK1 (Polo-like kinase 1 or Drosophila) are also highlighted. While PLK1 is currently less recognized as a biomarker in melanoma research, the importance of SPP1 in cutaneous pathology [315, 318, 320, 321] and in particular in melanoma [208, 209, 210, 211, 212, 214, 215, 216, 217, 218, 219, 222, 226, 264, 314, 315, 316, 317, 319, 322, 323, 324, 325, 326, 327, 328, 804, 805, 806, 807, 808, 809] is increasing. Using a 5-biomarker panel that included SPP1, Kashani-Sabet et al. used tissue microarrays on 693 melanocytic neoplasms to show that SPP1 expression collaborates significantly improving the detection of high percentage of melanomas arising in a nevus, Spitz nevi, dysplastic nevi and misdiagnosed lesions [253] . Like in the case of prostate cancer ( Figure 3 , in which KLK3/PSA -Prostate Specific Antigen was highlighted), our method allows the detection of important biomarkers with a high degree of concordance with current biological understanding of metastatic processes. doi:10.1371/journal.pone.0012262.g006 regarded as being linked to centrosome maturation and spindle assembly [135] . PLK1 expression has also been singled out as a biomarker of a ''death-from-cancer'' signature, sharing with others the function of being an activator of mitotic spindle check point proteins. With other proteins it would has a stem cell-like expression profile phenotypically characterized by enabling metastasis with anoikis resistance and disregulated cell-cycle control [136] . PLK1 inhibition could be a common target for gastric adenocarcinoma [137] , bladder cancer [138] , colon cancer [139, 140] , hepatocellular carcinoma [141] , medullary thyroid carcinoma [142] , esophageal cancer [143] , pancreatic cancer [144] and in some types of non-Hodgkin lymphomas [145] and breast cancer [146] .

PLK1's Spearman correlation with the values of the Jensen-Shannon divergence of samples with the normal skin profile is relatively high (0.5863). PLK1 also has a high value of (negative) Spearman correlation with the values of the Jensen-Shannon divergence of samples with the average metastatic profile (20.44571 In the comparison, it was found that metastatic malignant melanomas with expressed PLK1 at markedly elevated levels (median, 60.00% vs. 37.98%; p-value,0.000053), concluding that PLK1 is a reliable biomarker for patients at high risk of metastases, even when the most important prognostic clinical factor (Breslow's maximum thickness of the primary malignant melanoma) indicates the contrary [147] . We consider this an important finding as PLK1 silencing is already part of an integrated oncolytic adenovirus approach currently being studied in mice models of orthotopic gastric carcinoma [148] and has promise due to the lack of a reported measurable immune response of siRNA-based therapeutics [149] . Another positive note is the less sensitivity to PLK1 depletion of cells with a functional p53 [150, 151] , and can help to sensitize cells to chemotherapy (as observed in lung cancer [152] ). This constraint of aneuploid cancer cells to PLK1 expression, particularly in cells with inactivated p53 [153] , could be exploited by lentivirus-based RNA interference [154] .

Correlation analysis with Jensen-Shannon divergences reveals biomarkers for loss of cell adhesion, cell-cell communication, impairment of tight junction mechanisms and dysregulation of epithelial cell polarity.

As discussed before, the probe for ADA (Adenosine deaminase) is the first that has a different trend. Since we put all metastasis samples together in the same group when we calculated the average probability profile (and we have a heterogeneous group) we have on our ranking 58 probes that appear before ADA (we refer to the Supplementary File Haqq-PLoSONE-SupFile.xls). An analysis using GATHER (http://gather.genome.duke.edu/) [155] to interpret the collective influence of the lack of expression of all these genes in the metastasis samples reveals an interesting new perspective. Using Gene Ontology, we found that six of the 44 genes identified by GATHER are related to epidermis development (CDSN, DSP, EVPL, GJB5, KRT13, KRT5), p-value ,0.0001, Bayes Factor 16, and eight genes are related to cell adhesion (CDSN, CLDN1, DSG1, DST, LGALS7, LRIG3, PCDH21, PKP1), p-value,0.0001, Bayes Factor 7. ANK1 (Ankyrin 1, erythrocytic), AA464755 was also singled out as by our Gene Ontology analysis as related to the maintenance of epithelial cell polarity (p-value = 0.002, Bayes Factor 3). The use of another profiler of genome signatures (g:Profiler, [156] ) also reinforces the view that many genes that have lost expression are related to 'Epidermis Development' (COL17A1, DSP, EVPL, GJB5, KRT13, KRT5, LCE1C, MAFG, TGM3) with p-value = 7.78E-11. Thirteen are associated with Gene Ontology function of cell communication (ANK1, CDSN, CLDN1, DSG1, DST, GCHFR, GJB5, GPR115, LGALS7, LRIG3, PCDH21, PKP1, PTGER3), albeit with a pvalue of only 0.02. GCHFR is also involved in nitric oxide metabolism.

If we add to the list of 44 genes already recognized by GATHER the other 77 probes that after ADA in this ranking have also loss of expression (until we found PDXP (Pyridoxal (pyridoxine, vitamin B6) phosphatase), the evidence is stronger, now COL7A1, GJB5, KLK4, and KRT1 also is in this group (the Bayes factor of this association returned by Gather is now 21 for the GO term 'Epidermis development'). 'Cell adhesion' has now 13 genes, CDSN, CLDN1, COL7A1, DSC2, DSG1, DST, JUP, LGALS7, LRIG3, PCDH21, PKP1, SLIT3 THBS3 (p-value,0.001, Bayes factor 10). These results are considered statistically very relevant as identifiers of a particular process which seems to be undermined by this collective loss of expression.

If we put all this information together, we clearly observe a pattern of downregulation of gene expression that is associated with an impairment of epidermis development and the maintainance of its structure ( Figure 8 and Table 1 ). This is, perhaps, an instantiation of one of the ''extended hallmarks of cancer'' (that of ''tissue dedifferentiation''). This process includes the loss of function of genes that are essential for the maitainance of tight junction and epithelial cell-cell communication. While loss of epithelial structure is related to these genes, we observe that those that increase expression are associated to other developmental processes, not necessarily concerted in this panel. Instead they show a pattern of increasing cell motility, chemotaxis and positive regulation of cell proliferation. We will first discuss the processes related to the loss of adhesion, which could be linked to an increased probability of metastatic potential of these cells.

The loss of expression of Plakophilin 1, Junction plakoglobin, Desmoplakin and Desmoglein 1 indicate deficiencies in desmosome processes.

In general, this panel is composed of a number of genes that are losing expression during progression and that have Gene Ontology annotations related to tight junctions, gap junctions, adherens junctions and desmosomes, and an impaired set of processes that link, via intercellular channels and bridges, the cells of the epidermis. Mutations in these genes are linked to a number of skin genetic diseases [157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170] The desmosome are cell-cell adhesive junctions which provide a mechanical coupling between cells. These junctions are found in several epithelial tissues and the decreased assembly of the desmosome has been shown to be a common feature of many epithelial cancers [171, 172] . Plakoglobin helps to connect transmembrane elements to the cytoskeleton [173] . Plakophilin 1 [174] (PKP1, one of the genes in our panel above) is a desmosomal plaque component [175] that stabilizes desmosomal proteins at the plasma membrane [176, 177] and, with desmoplakin [178] , recruits filaments to sites of cell-cell contacts [179] . As a consequence, it has been proposed that the lack of PKP1 increases keratinocyte migration [180] and loss of PKP1 expression in head and neck squamous cell carcinoma and in esophageal squamous cell carcinoma may contribute to an invasive phenotypic behaviour [171] , perhaps as a consequence of the impaired recruitment of desmoplakin.

The desmoglein-specific cytoplasmic region (DSCR) is the site of caspase cleavage during apopotosis and is a conserved region of yet undefined function and unknown structure, but it specifies the function of the desmoglein family of cell adhesion molecules (of which DSG 1 is a member). It has been recently shown that the DSCR has a weak interaction with PKP1, Plakophilin 1 (ectodermal dysplasia/skin fragility syndrome) and the cytoplasmic domain of Desmocollin 1 [181] . Plakoglobin is cleaved by Caspase 3 during apoptosis [182] . In addition, Kami et al. in Ref [181] also report and conclude that: ''desmoglein 1 membrane proximal region also interacts with all four DSCR ligands, strongly with plakoglobin and plakophilin and more weakly with desmoplakin and desmocollin 1. Thus, the DSCR is an intrinsically disordered functional domain with an inducible structure that, along with the membrane proximal region, forms a flexible scaffold for cytoplasmic assembly at the desmosome''.

As previously discussed, all these genes progress towards a loss of expression, and they are highly correlated. Figure 9 shows the average expression of PKP1/Plakophilin 1 (ectodermal dysplasia/ skin fragility syndrome), (NM_000299) and JUP, Junction plakoglobin, (BX648177) on the x-axis against that of DSP, Desmoplakin (NM_004415 Hs.519873) on the y-axis. Again, we see a clear pattern of progressive reduction of expression from normal skin and nevi (green and yellow, respectively), primary melanomas (in orange) and melanoma metastases (red).

Joint loss of expression of Claudin 1 and members of the Aquaporin family are also linked to a transition to a more malignant phenotype

We note however, the Gene Ontology annotation is not the only way that we can make sense of this information. A detailed analysis of that list of 58 genes reveals other proteins involved in tight junction, like Aquaporin 3 (AQP3). Probes for AQP3 and Claudin 1 (CLDN1) have reduced expression with the progression of the disease as shown in Figure 10 .

AQP3 (Gill blood group) is a member of the aquaporin family of proteins, and currently is recognized as an 'aquaglyceroporin' [183] of great importance to maintain skin hydration of mammals epidermis [184] . Three proteins of this family (AQP1, AQP3, and AQP9) have probes that seem correlated with melanoma progression, all losing their expression in the process of going from normal skin to metastatic melanoma. AQP3 water channels have been pointed out as an essential pathway for volume-regulatory water transport in human epithelial cells [185] . AQP3 is also selective for the passage of glycerol and urea and it has been suggested that osmotic stress up-regulates AQP3 gene expression in cultured keratinocytes [186] . AQP3 was found to be the predominant aquaporin in human skin which increased expression and altered cellular distribution of AQP3 in eczema thus contributing to water loss [187] . The putative involvement of aquaporins in the progression of melanoma, uncovered by our method in our results, warrants further investigation as it has been recently shown that another member of this family (AQP8) also facilitates hydrogen peroxide diffusion across membranes [188] . It is suspected that AQP3 has other functions with a suggestion that it is involved in ultraviolet radiation induced skin dehydration [189] . There is no probe for AQP8 in Haqq et al.'s dataset that we could scrutinize from its trend with progression but we note that a novel strategy for drug development for melanoma (i.e. Elesclomol) works by inducing apoptosis via a mechanism of elevation of reactive oxygen species (of course, including hydrogen peroxide in cancer cells) thus exploiting the ''Achilles hell of cancer metabolism'' [190] .

Claudin 1, CLDN1 [191] , a gene which is reported to be ''normally expressed in all the living layers of the epidermis'' [192] , in concert with AQP3, is a key component of the tight junction complexes of the epidermis. Low CLDN1 gene expression was correlated with shorter overall survival in lung adenocarcinoma. Overexpression of CLDN1 was correlated with suppression of cancer cell migration, invasion and metastasis [193] . Hoevel et al. report that re-expression of CLDN1, in breast tumor spheroids, induces apoptosis and they conclude: ''These findings support a potential role of the tight junction protein CLDN1 in restricting nutrient and growth factor supplies in breast cancer cells, and they indicate that the loss of the cell membrane localization of the tight junction protein CLDN1 in carcinomas may be a crucial step during tumor progression'' [194] . Tokes et al.also report that malignant invasive breast tumors are negative Table 1 . Gene names and probe accession number of the 27 probes with genes annotated with functions on cell adhesion, cell-cell communication, tight junction mechanisms and epithelial cell polarity shown in the heat map in Figure 8 . for CLDN1 [195] . As in breast cancer [196] , in which reduced expression correlated with recurrence status, the low expression of CLDN1 and other tight junction proteins seems to contribute to cellular detachment.

The complementary set of correlations with the Jensen-Shannon divergences unveils biomarkers for cell proliferation, chemotaxis, and responses to external simulus.

If the use of Gene Ontology has produced very peculiar results, helping us to link the loss of expression of 44 genes with a significant change in epithelial structure and development. A natural question arises: ''Which is the significance of another set, now arbitrarily chosen to be also of the same cardinality (i.e 44 genes) with the complementary behavioural pattern?'' We have now listed all the probes according to Diff. (probe) = JSM0(probe)2JSM5(probe) in decreasing order. The results are provided as Haqq-PLoSONE-SupFile.xls ('Results-correlation' sheet). This now gives ADA as the first ranked gene. Again using GATHER [155] on the first 44 genes recognized by the software, and again using Gene Ontology, we observe as most important common function that of cell motility (CCL3, CXCL10, FPRL1, SEMA6A, SPP1), p-value = 0.0002, Bayes Factor 5, and chemotaxis (CCL3, CKLFSF7, CXCL10, FPRL1, SPP1), p-value,0.0001, Bayes Factor 7. The genes CXCL10, SPP1, and WARS, together with another gene that has been annotated as related to positive regulation of mitosis (SCH1), have also been annotated as regulators of cell proliferation (pvalue = 0.007, Bayes Factor 2). Using the g:Profiler software [156] , we obtain a complementary information. Sixteen genes (including SPP1, SEMA6A, LEF1 [197] , CD230, ALS2CR2, DKK1, CYFIP2, SHC1, ANKRD7, IFI6, CITED1, and MID1) have been associated to the Gene Ontology term of 'developmental process'.

SPP1 -Secreted phosphoprotein 1 (osteopontin). SPP1 is one of the most conspicuous melanoma biomarkers [198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222] (see also the references cited in Figure 6 and note its eminent position in this scatter plot). In 1990, Craig et al. reported that SPP1 may work as an autocrine adhesion factor for tumor cells (see also [204, 223, 224] ). They observed that ''SPP1 mRNA, which is barely detectable in normal mouse epidermis, was expressed at moderate-to-high levels in 2 of 3 epidermal papillomas and at consistently high levels in 7 of 7 squamous-cell carcinomas induced by an initiation-promotion regimen'' [225] . The evidence is being constantly expanded on the role of SPP1 as a molecular prognostic biomarker in melanoma [226] . Activation of SPP1 may be an important event that allows the transformed melanocytes to invade the dermis as proposed by Geissinger et al. in 2002 [208] . This causes SPP1 to avoid the apoptotic stimulus, one of the ''hallmarks of cancer'', which invasive cells will be receiving from this new tissue.

If we extend the literature-based search so that we now include the first 200 gene probes recognized by GATHER then we have 27 gene probes associated with the Gene Ontology in terms of ''cell proliferation'' (p-value = 0.0002, Bayes Factor 5), and 'regulation of cell proliferation', p-value = 0.003, Bayes factor 3). However, other partners of PLK1 appear and their function in 'mitotic cell cycle' (pvalue = 0.0003, Bayes Factor 5) is increasingly present (in particular, the M phase of the mitotic cell cycle). The details of the Gene Ontology terms which are significant and the genes associated to them are listed in Table 2 .

The analysis using g:Profiler largely coincides with the analysis using GATHER, however, it retrieves 12 genes associated with the M phase of mitotic cell cycle, namely: AURKA and AURKB [227, 228, 229] , BUB1 [230, 231] , CDCA5A/Sororin/p35 [232] , CDC7 [233, 234] , CHEK1 [235] , KIF23/MKLP-1 [227, 236, 237] , MAP9/ASAP [238, 239] , NCAPD3, NCAPG2 [240] , NEK6 [241, 242, 243, 244] , PLK1 [147, 245, 246] , PTTG1/Securin [247] , SHC1/p66 [248, 249, 250] (discussed in the context of SHC4 signalling), and TFDP1/DP-1 [251] . These are a significant finding by g:Profiler (p-value = 4.03E-07).

We have listed above some of the genes gene associated to the M phase of mitotic cell cycle and associated references which are either to current research in melanoma and/or its biological function. We now list other genes which have been associated with the term 'cell proliferation' by GATHER. These genes are: ARPC1B [252] , ARPC2 (which, together with SPP1, is also in the novel 5-biomarker panel of Kashani-Sabet et al. [253] ), BCCIP (BRCA2 and CDKN1A-interacting protein)/P21-and CDK- Figure 10 . Expression of a probe for CLDN1 (Claudin 1) (y-axis) as a function of a probe for Aquaporin 3 (x-axis). Other members of the aquaporin family of proteins have a similar behaviour. AQP3, together with CLDN1 are key components of the tight junction complexes of the epidermis and their joint loss of expression seem to be related to a transition to a more malignant phenotype. We use the same color coding as Figure 9 . doi:10.1371/journal.pone.0012262.g010 associated protein 1) [254] , BST2/Bone marrow stromal antigen 2/Tetherin [255] , CCL3/MIP-1alpha [256, 257, 258] , CCT4, CDCA5/Sororin [259, 260, 261, 262, 263] , CENPF/Mitosin [264] , CXCL1/chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity, alpha) [265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285] (in uveal melanoma see [286] ), CXCL10 [256] , FLT1/VEGFR1 [287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299] , FTH1/Ferritin Heavy Chain [300, 301, 302] (which may indicate a necessary condition for the mainainance of iron sequestration and suppression of reactive oxygen species accumulation [303] ), FPRL1, LIG3/DNA Ligase 3 [304] (which, together with XPA and ERCC5 is associated to DNA repair in ionizing radition studies [305] ), MCMDC1, PSEN2, NRP2/Neuropilin 2/Vascular endothelial cell growth factor 165 receptor 2 [306, 307, 308] , SEMA6A (a member of the Semaphorin family, of increasing importance in cancer research [309, 310, 311] and in particular due to its observed upregulation in undifferentiated embryonic stem cells [312] ), SLAMF1/CD150 (a marker associated with hematopoietic stem cells [313] ), SPP1/Osteopontin (which, together with ARPC2, is also in the novel 5-biomarker panel of Kashani-Sabet et al. [253] ) [206, 207, 208, 209, 210, 211, 212] [206,207,208,209,210, 211,212,214,215,216,217,218,219,220,221,222,226,314,315,316,-317,318,319,320,321,322,323,324,325,326,327,328,329] , STK6 [230, 330] , and WARS/Tryptophanyl-tRNA synthetise [331] . Figure 11 shows a heat map of discussed gene probes annotated with functions on cell proliferation.

The references provided next to each gene help to related these upregulated genes in the context of current research in melanoma or with the M phase of mitotic cell cycle, showing a high degree of correlation between our results and with published literature.

Another microarray dataset we have selected to evaluate for the relevance of transitions of Normalized Shannon Entropy and Statistical Complexity was contributed by True et al. [332] in 2006.

The original goal of True et al. was to identify a molecular correlate for Gleason patterns 3 and, if possible, the clinically most worrisome patterns 4 and 5. They partially succeeded by linking the expression of only 86 genes with Gleason pattern 3 [332] using a standard statistical analysis. In this study, we eliminated sample 02-209C since data was acquired using a different platform and would not be useful for our analysis. The remaining thirty one (31) samples were assayed with the GPL3834 (FHCRC Human Prostate PEDB cDNA Array v4) platform using 15,488 probes.

We also eliminated all the probes with missing values, remaining 13,188 probes.

We have first plotted the samples on the (Normalized Shannon Entropy, MPR-Statistical Complexity) plane ( Figure 12 ). It was interesting to observe that there exists a high correlation between the two measures. Samples that are entirely composed of Gleason pattern 3 tend to have a greater value of Normalized Shannon Entropy than 0.985. We can also identify a cluster of samples that present Gleason patterns which are either 4 or 5. Note that there seems to be two outliers (02_003E and 03_063) to the generic trend of the other 29 samples. The two outliers are samples that correspond to samples labelled as having Gleason 3 patterns and both have unusually low values of Normalized Shannon Entropy that are well below the values of the rest of the group.

This raised a suspicion about the true nature of this phenomenon. If the labelling is correct, this may indicate a subsampled group of prostate cancer that has Gleason 3 pattern characteristics but very low entropy. Alternatively, it may indicate an experimental bias for reasons we can not explain with the available clinical information. In order to clarify the situation, and see if we can declare these two samples as outliers of the other group, we performed another experiment. We have now computed two modified complexities, which we will call M- Gleason 3 and M-Gleason 5 ( Figure 13 ). The names are probably selfexplanatory, but a brief reminder follows. To calculate the MPR-Complexity, by definition, we have used the equiprobable distribution as our probability distribution of reference (for the computation of the Jensen-Shannon Divergence of the gene expression profile to this distribution). In the case of the M-Gleason 3, the probability distribution of the reference is obtained averaging all the probability distributions of the samples that have been labelled as Gleason 3 (analogously, we calculated M- Gleason 5) . Samples that have Gleason pattern 3 and 5 appear as separate clusters in the (M-Gleason 3, M-Gleason 5) plane with the two putative outliers of the general trend far apart (even if they have been used to calculate the average probability distribution function of the Gleason 3 pattern). Even samples with Gleason 4 pattern are located closer to samples of Gleason patterns 3, and 5, indicating that, perhaps, there exists a subsampled subtype of prostate cancer or there might be another experimental bias or factor that at present we can not resolve with the information we have for these samples. Consequently, we have decided to eliminate both samples (02-003E and PNA_03-063A) from further calculations. With these considerations, we now have a dataset with 13,188 probes and 29 samples as our dataset for further analysis. Figure 14 shows the distribution of the samples using the Normalized Shannon Entropy and the MPR-complexity. By definition, the positions of the 29 samples in the plane do not change (this figure is basically ''zooming in'' one region of Figure 12 that contains these samples). We note again, however, that the 29 samples seem to be separating in three different clusters. Whether we can argue about the existence or not of these gaps in Normalized Shannon Entropy, it is clear that there seems to be a progression as we have seen with Lapointe et al's dataset. There is a group of three samples with Gleason pattern 3 that seem to have the the largest Normalized Shannon Entropy values. There is also a cluster that only contains samples of either Gleason pattern 4 and 5, all with Normalized Shannon Entropy values smaller than 0.985.

There is also very little variation (see Figure 15 ) of the positions of the 29 samples on the (M-Gleason 3, M-Gleason 5)-plane, indicating a degree of robustness that the computation of these modified complexities have, even in the presence of some outliers.

After observing that Figure 14 shows a correlation of Gleason pattern score with Normalized Shannon Entropy, we asked ourselves: 'which are the genes that most positively and negatively correlate with the transitions of Normalized Shannon Entropy?' We have plotted Spearman versus Pearson correlation values of probe expressions to attempt to find those that best correlate, either positively or negatively, with the Normalized Shannon Entropy values of the samples. The results have revealed some of the most relevant biomarkers of progression, and some unexpected newcomers. Figure 16 shows the Pearson and Spearman correlations of all the 13,188 probes in the dataset with the Normalized Shannon Entropy values of the samples. We have highlighted some particular genes that are discussed below.

CDKN2C (cyclin-dependent kinase inhibitor 2C (p18, inhibits CDK4). When we compute the correlations of the probes expressions with the Normalized Shannon Entropy values of the samples, the gene that has the most negative correlations is CDKN2C (cyclin-dependent kinase inhibitor 2C -p18, inhibits CDK4 -NM_078626), which has been previously associated with the transition from prostatic intraepithelial neoplasia (PIN) to prostate cancer [68] Figure 12 . We plot the values of two modified statistical complexities, which we will call M-Gleason 3 and M- Gleason 5. Instead of using the equiprobable distribution as our probability distribution of reference (for the computation of the Jensen-Shannon Divergence of the gene expression profile to this distribution), as required for the MPR-Statistical Complexity calculation, we used a different one. For the M-Gleason 3, the probability distribution of the reference is obtained averaging all the probability distributions of the samples that have been labelled as Gleason 3 (analogously, we calculated M- Gleason 5) . This is analogous to our approach in melanoma ( Figure 5 ) in which we used normal and metastatic samples as reference sets for a modified statistical complexity. We observe that, even in this case, 02_003E and 03_063 continue to appear as outliers. In addition to the evidence, we have observed that the deletion of these two samples did not significantly alter the identification of biomarkers. doi:10.1371/journal.pone.0012262.g013 Samples 02_003E and 03_063 seem to be outliers to this trend, and in the case of 03_063 the sample is not even close to a hypothetical linear fit which seems to be the norm for all the samples. Figure 13 will provide further evidence that may indicate that these two samples are outliers or not to the overall trend. doi:10.1371/journal.pone.0012262.g012 with the dedifferentiation process, with preoperative PSA levels and the percent of Gleason 4 and 5 cancers [338] .

AMACR, Cyclin G2, CDK4 and CDK7. Other probes that also have high negative correlations with the Shannon Normalized Entropy correspond to CCNG2 (Cyclin G2) CR598707, CDK4 (Cyclin-dependent kinase 4), CDK7 (Cyclin-dependent kinase 7, TFIIH basal transcription factor complex kinase subunit) [339] , and AMACR (Alpha-methylacyl-CoA racemase), an ''obscure metabolic enzyme (that has taken) centre stage'' [340] as judged by the extraordinary convergence to this biomarker in prostate. We believe that our result is an important finding. AMACR was not judged of importance according to the methodology used in [332] and it was barely cited in that manuscript. Here we present results, from an unifying biological and informational principle, which allows (using Ref. [332] 's own data) the identification of the most central current biomarker with a truly compelling body of support in independent studies [ [490, 491] . Knockdown of BRCA1 results in the accumulation of multinucleated cells, indicating that BRCA1 regulates gene expression of an orderly progression during mitosis [492] , preserving chromosomal stability [490] . BRCA1 showed decreased expression in a study involving immortalized prostate epithelial cells before and after their conversion to tumorigenicity [493] . Lack of BRCA1 function may impair activation of STAT3 [494] . Inactivation of TP53 by somatic mutations is also associated to the panel of disruptions which are common for this ''tumor suppressor'' [113] . One possible mechanism for gene silencing is CpG island methylation. Rabiau et al.show in [495] that BRCA1, RASSF1, GSTP1 and EPHB2 promoter methylation is common in prostate biopsy samples. Mannicia et al. suggest that the mitochondrial localization of BRCA1 proteins may be a significant factor in regulating the mitochondrial DNA damage [5] .

SFPQ -(Polypyrimidine tract-binding protein-associated splicing factor). The most positively correlated gene with the loss of Normalized Shannon Entropy is SFPQ/PSF (Polypyrimidine tract-binding protein-associated splicing factor) (Spearman correlation of 0.7902), a multifaceted nuclear factor [496, 497] which is also a putative regulator of growth factor-stimulated gene expression [498] . This is extremely interesting as it has been recently shown that the AR/PSF complex interacts with human PSA gene and that PSF inhibits AR transcriptional activity [499] . The loss of expression of SFPQ and other proteins that together regulate androgen receptor-mediated gene transcription [500] (see also [501, 502] ) may indicate they have a role not only as a biomarker of the progression and well as transitions of the disease to androgen independence. In a study of human labor, Dong et al., also showed that SFPQ acts as a Progesterone Receptor corepressor, thus putatively contributing to the functional withdrawal of progesterone [503] . We will return to this particular gene later on the 'Discussion' section as new evidence of its role in nuclear organization has been documented.

CD40 -(TNFRSF5, B-cell surface antigen CD40). The loss of Normalized Shannon Entropy gives us several markers that indicate a de-differentiation from a epithelial basal phenotype and an increasing loss of control of cell cycle regulation (due to uncoordinated upregulation of CDK4, CDK7, CCNG2 with their functional partners). This poses the question: What can we observe while looking at the genes that most positively correlate with the loss of Normalized Shannon Entropy? We observe, second on the ranking of all samples, a probe for CD40 (TNFRSF5, B-cell surface antigen CD40), BX381481 with a Spearman correlation of 0.7616. Loss of CD40 expression has been previously reported in prostate cancer and it is the object of a study that attempts to establish dendritic cell gene therapies [504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522] . We will continue discussing CD40 in the following subsection in concert with other genes.

Another natural question can be asked: Which is the extra information that we can obtain the by analysing the correlations with the MPR-Statistical Complexity in this case? As we have discussed before, and can be appreciated from Figure 14 , there is a strong correlation between the MPR-Statistical Complexity and the value of the Normalized Shannon Entropy. It appears in prostate cancer, as in this gene expression dataset, the reduction of Entropy is not the major factor responsible for the increase in MPR-Statistical Complexity. Again, it is perhaps better to now look at one of the multiplicative factors of the statistical complexity measure, the Jensen-Shannon divergence to the equiprobability distribution, as this is increasing the MPR-complexity.

CD40. We present more evidence of the case of CD40 as a biomarker, since a probe for CD40 (BX381481) ranks 6 th (the Spearman correlation of the probe expression with the Jensen-Shannon divergence from the equiprobability distribution is 20.5764). CD40 is a member of the TNF receptor superfamily. Notably, in 56 out of 57 archival prostate cancer samples Palmer et al. have reported no CD40 expression [518] . However, CD40 expression was present in normal prostatic acini, so they proposed that ''invasive prostate cancer is a CD40-negative tumour'' (see the previous results of Moghaddami et. al. [514] ). Matching our observations, they proposed that CD40 provides ''insight into progression of cancer from normal epithelium''; our proposed methodology is revealing this fact as well. Depletion of CD40 in the tumour microenvironment may be central in avoiding the action of the immune system [506] , as prostate cancer induces a progressive suppression of the dendritic cell system [520] . It is perhaps a central piece which should be put together in the context of other pieces of information coming from immunotherapy [508, 512, 513, 516] and pharmacological studies [507] that warrant serious investigation towards the design of new and improved clinical studies [508, 517] .

CD59 molecule, complement regulatory protein. Four probes for protectin [335, 523, 524] , CD59, with Spearman correlations with the Jensen-Shannon divergence from the equiprobable distribution, ranging from 20.61823 to 20.5089, rank between the 1 st and 39 th position (when we rank genes according to this correlation in ascending order). CD59 is an interesting gene as ''a comprehensive investigation of CD59 expression in prostate cancer has not been conducted yet'' [524] . Like LMNA (which is ranked third and will be discussed later) the rank of CD59/ protectin means that these genes progressively loose expression of these probes. CD59 is expressed in the prostatic epithelium [525] and in prostasomes [526] ; secretory granules which are produced, stored and released by the glandular epithelial cells of the prostate [527] . Babiker et al. concluded in [335] that prostasomes (via expression CD59) contribute to the protection of malignant cells from complement attack. We now investigate if the ratio of deltacatenin to CD59 can is a more robust biomarker for non-invasive prostate cancer detection, particularly after the results presented in [528] . We also note that CD59 may be also relevant to reveal the heterogeneous nature of prostate cancer. Its correlation was good, but is not lower than 20.62, which in our experience, indicates that we may be dealing with at least two types tumors in this dataset. Indeed, Xu et al. obtained CD59 mRNA levels were determined by real-time PCR in matched (tumor/normal) microdissected tissues from 26 cases and they found that: ''High rates of CD59 expression were noted in 36% of prostate cancer cases and were significantly associated with tumor pT stage (P = 0.043), Gleason grade (P = 0.013) and earlier biochemical (PSA) relapse in Kaplan-Meier analysis (P = 0.0013). On RNA level, we found an upregulation in 19.2% (five cases), although the general rate of CD59 transcript was significantly lower in tumor tissue (P = 0.03)'' [524] . They concluded that: ''CD59 protein is strongly expressed in 36% of adenocarcinomas of the prostate and and is associated with disease progression and adverse patient prognosis'' [524] . Jarvis et al. have previously hypothesized that CD59 expression, in some cancer cells, may help to regulate the immunological response, protecting them from the cytolytic activity of complement [523] (see also [529, 530] ).

LMNA (Lamin A/C). The third probe in the ranking corresponds to a LMNA (Lamin A/C), AY528714. Mutations on LMNA have been linked at 10 different human diseases [531, 532] . LMNA, due to its functions, could be involved in important cell fate decisions as lamins are involved in the organization of the functional state (and position) of interphase chromosome [531] . Lamins are ''scaffolders'' for the function of nuclear processes such as chromatin organization, DNA replication, cellular integrity and transcription [532] . As a consequence Lamins are involved in several clinical syndromes [533, 534, 535] . Among the recent functions attributed to LMNA is as an intrinsic modulator of ageing within adult stem cells via a mechanism where LMNA act as signalling receptors in the nucleus. These observations correspond to Pekovic and Hutchinson who observed that dysfunction of LMNA leads to inappropriate activation of self-renewal pathways and initiation of stress-induced senescense [536] . In lmna-deficient mouse embryonic fibroblasts (lmna(2/2) MEFs), the loss of lmna''dramatically affects the micromechanical properties of the cytoplasm'', since ''Both the elasticity (stretchiness) and the viscosity (propensity of a material to flow) of the cytoplasm in Lmna(2/2) MEFs are significantly reduced'' [537] . Using ballistic intracellular nanorheology to evaluate the micromechanical properties of the cytoplasm of these cells, Lee et al. conclude: ''Together these results show that both the mechanical properties of the cytoskeleton and cytoskeleton-based processes, including cell motility, coupled MTOC and nucleus dynamics, and cell polarization, depend critically on the integrity of the nuclear lamina, which suggest the existence of a functional mechanical connection between the nucleus and the cytoskeleton. These results also suggest that cell polarization during cell migration requires tight mechanical coupling between MTOC and nucleus, which is mediated by lamin A/C'' [537] (see also [538, 539] ). In addition to these very interesting findings, a functional association of LMNA and the retinoblastoma protein (pRB) exists. Nitta et al. have shown that pRB needs to be stabilized by LMNA for INK4A-mediated cell cycle arrest and that somatic mutations in LMNA may also have a role in tumor progression [540] . In mammalian cells, LMNA a) colocalizes with c-FOS at the nuclear envelope, b) suppresses AP-1 through a direct interaction with c-FOS and, in LMNA-null cells perinuclear localization of c-FOS is absent (but it is restored when it is overexpressed, c) LMNA-null cells have enhanced proliferation [74] . These results obtained by Ivorra et al. are giving the indication that of yet another mechanism of cell cycle and transcriptional control mediated by LMNA [74] (see also [541] ). LMNA has also been proposed as an inhibitor of adipocyte differentiation [542] . Hutchingson et al. have proposed the alias of ''guardian of the soma'' for lamins A and C as they seem to have ''essential functions in protecting cells from physical damage, as well as in maintaining the function of transcription factors required for the differentiation of adult stem cells'' [543] .

From our results, we can not completely establish if the downregulation of CD40 and CD59 are enough to pinpoint an impaired or abnormal immune response. If we continue the inspection of the list, the first 20 probes give us more supporting evidence. The 20 probes correspond to 13 different genes. Five of these 13 genes have Genome Ontology information annotated as ''defense response'', the above mentioned CD59 and CD40 as well as IL4R (interleukin 4 receptor, CR616481), XBP1 (X-box binding protein 1, AK093842) and HLA-A (major histocompatibility complex class I HLA-A29.1, BU075230). Takahashi et al. [544] report an inverse correlation between XBP1 expression and histological differentiation in a series of prostate cancers without hormonal therapy, the expression of XBP1 was localized in epithelial and adenocarcinoma cells of the prostate and the majority of refractory cancer cases exhibited weak XBP1 expression), MST1/STK4 (along with MST2/STK3) act as inhibitors of endogenous AKT1, a mediator of cell growth and survival [545] .

We can not yet know what reason is behind their joint downregulation, but another interesting common denominator is that 12 out of 13 genes share a regulatory motif for NF-kappaB (according to TRANSFAC, V$NFKB_Q6_01). A putative role for NF-kappaB in prostate cancer has been reported based on the observation of the centrality of NFKB on two up-and downregulated networks compairing prostate tumors and healthy tissue [546] and in a larger study by McDonnel et al. [547] (255 core prostate cancer tissue microarrays from 47 prostatectomy specimens). Several other researchers are currently investigating different roles of the NFKB family in prostate cancer [548, 549, 550, 551, 552, 553, 554, 555] and it could be a promising target for intervention [555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571 ]. If we include other genes following the ranking order, the first 38 genes in the ranking include 33 that have the regulatory motif V$NFKB_Q6_01 (GATHER reports for this list a p-value of 0.0006). Even when we double the list to the probes that correspond to the first 76 different genes recognized by GATHER, 58 of them have the regulatory motif V$NFKB_Q6_01, with p-value = 0.003 (ATP6AP2, BCAT1, BTG2 [572, 573, 574, 575, 576, 577, 578] , C14orf123, C18orf45, CCL2, CD302, CD40 (already discussed), CD59 (already discussed), CHI3L1, COL16A1, COMMD6, CRABP2, CSRP1, CTBP2, CTGF (Connective tissue growth factor, [579, 580, 581, 582] ), DES, DMN, DNAJB1, EGF, EMP1, FHL2 [583, 584, 585, 586, 587, 588] , GRIPAP1, GSTM1 [589, 590] , HBEGF, IL4R, ITGA3, ITGA7, JUNB [591, 592] , KIAA0152, KIAA1191, KIAA1324, KLF6, LAMB2, LMNA (already discussed), NFATC1, NFKB2, NUDC [593] , P4HB, PDK2, PIM1, PISD, PXN, RAP1B, RNF40, SARA1, SEC61A1, SGTA [594] , SLC12A2, SRD5A2, STAT6 [595, 596] , TACSTD2, TBX1, TMED3, VPS39, WDFY3, XBP1 [544] , ZAK). This result indicates that our results support the importance of NFkappa-B and the huge amount of research effort to understand the role of the NFkappa-B activity and its potential as a target for intervention in prostate cancer (File S4).

The group of 58 biomarkers contains one of particular interest, STAT6. This gene is considered a survival factor in prostate cancer and a key regulator of the genetic transcriptional program responsible for progression [595] . STAT6 has been recently linked to HPN as one of the most robust pair of biomarkers for prostate cancer using an integrative approach that linked several microarray datasets [596] .

Analysis using GATHER of this group reveals that six of these 58 genes are in KEGG pathway path:hsa04510, Focal adhesion (EGF, ITGA3, ITGA7, LAMB2, PXN, RAP1B, p-value,0.0007) and from these there are three in pathway:hsa045122, ECM-receptor interaction (ITGA3, ITGA7, LAMB2, p-value,0.005) while four of these six are also in path:hsa04810: Regulation of actin cytoskeleton, (EGF, ITGA3, ITGA7, PXN, p-value,0.01).

LAMB2. Alterations of the gene profile of LAMB2 and CDKN2C/p18(Ink4c), a CDK4 inhibitor, have been reported on the transition from prostatic intraepithelial neoplasia (PIN) to prostate cancer [597] (see also [333] ).

3). The contribution of the loss of these integrins and the subsequent derived impairment on cell adhesion has been reported in several tumours. Ren et al. in [598] report that ''Focal or no integrin alpha 7 eexpression in human prostate cancer and soft tissue leiomyosarcoma was associated with a reduction of metastasis-free survival (for example, for prostate cancer with focal or no expression, 5-year metastasis-free survival was 32%, 95% CI = 24.4% to 40.3%, and for prostate cancer with at least weak expression, it was 85%, 95% CI = 79% to 91%; p-value,.001)''.

''Any method involving the notion of entropy, the very existence of which depends on the second law of thermodynamics, will doubtless seem to many far-fetched, and may repel beginners as obscure and difficult of comprehension.'' Willard Gibbs, Graphical Methods in the Thermodynamics of Fluids, (1873)

The changes of the Normalized Shannon Entropy and Statistical Complexity of the gene expression profile of a cancer cell are associated with the gradual deterioration of genome transcriptional information content due to the modification of its structural and functional integrity during disease progression. Our results clearly suggest that we can track the cancer cell's progression by following observable changes in the Shannon Entropy and, in particular, by employing the Jensen-Shannon Divergence of the gene expression profile of a sample to the normal expression profile. We have also shown if an average expression profile of some state of interest can be properly defined (i.e. distant metastasis) then the Jensen-Shannon Divergence can help us to identify which probes best correlate with these measures resulting in useful biomarkers.

Before any thermodynamical consideration could be discussed, we note that there is a clear and objective informational perspective that our study delivers. In this study we have chosen to position ourselves as the 'receivers' of a 'transcriptional message'. In this experimental perspective the tumor tissue is the 'sender' (the source of information) and the high-throughput technology (gene expression microarrays in this case) can be regarded as the transmission medium (providing noise and distortion). As we explain in the 'Materials and Methods' section, the Shannon Entropy of a gene expression profile is the average expected surprisal of that profile understood as a message. The Normalized Shannon Entropy makes this surprisal an intensive measure and the correlation of the gene expression patterns across samples with this measure can deliver useful biomarkers to track the progression of transcriptional change. After normalization, we have a measure that does not depend of the number of probes of the high-throughput technology, although, it obviously does depend on the type of probes used.

We believe that the readers may have already noticed an apparent paradox. While some researchers understand cancer progression as a mechanism that increases entropy, we actually observe a reduction of Normalized Shannon Entropy in this work. This means that our normalized average expected surprisal, as receivers of the transcriptional message, is smaller. We must then discuss the physical meaning of thermodynamic entropy, its current use in systems biology and cancer research genetics and the informational measure we use in this paper to clarify these notions in this context.

In biomedical research there exists a certain consensus among cancer researchers that genetic instability or ''mutability'' is a major critical force of cancer progression, but it is not the only one to consider. It is clear that the mutational damage of key genes (like TP53, TERT, BRCA1, RB1, etc.), and the collective damage inflicted on key DNA repair mechanisms (like Nucleotide-excision repair and Base-excision repair) collaborate for an increasing acceleration of the number of genomic changes. Sub-microscopic alterations of the genome accumulate in cancer progression in an irreversible way and ''are compounded by the widespread scrambling of the chromosome structure, and thus the karyotype, found in cells from the great majority of solid tumours'' [599] . In Weinberg's own words [599] : ''we learned that this chromosomal chaos also contributes this progression forward''.

This ''chromosomal chaos'' [600] or ''cancer as a chromosomal disease'' perspective is viewed by some researchers not as just a side consequence of mutational damage, but as the main core theme to understand a number of unexplained issues in cancer progression. ''In sum, cancer is caused by chromosomal disorganization, which increases karyotypic entropy'' [601] . Regarding the cancer types studied in this paper, one particular ''measure of disorder of a system'', aneuploidy, has been observed in poorly-differentiated prostate cancer cells and it is often associated with a more agreessive phenotype [602, 603] , increased PSA levels [604, 605] , and correlate with Gleason score [606, 607, 608] . Gene fusions and chromosomal rearrangements are other source of increase in the ''disorder'' of the genome organization and they are increasingly being recognized as a major player in prostate cancer progression [609] . The increase in ''karyotypic complexity'' and ''extended aneuploidy and heteroploidy'' may be already enough to develop a malignant melanoma phenotype, as the report of Gagos et al. indicate [610] . The observed finding of aneuploidy in melanoma (also including uveal melanoma) is also increasingly important due to a number of different independent observations [247, 611, 612, 613, 614, 615, 616, 617, 618] . It is in this context that the word 'entropy' has been used.

The magnitude of the ''chromosomal chaos'' is also evident from comparative genomic hybridization (CGH) studies which show significant variations in the copy number of individual chromosomal segments. 'Chaos' is really a very appropriate word to describe what we observe from CGH data. The genomic changes are not distributed uniformly at random. 'Chaos' has been described by some researchers as ''a kind of order without any periodicity''. Some common changes seem to consistently appear in several independently arising tumours of the same type, and sometimes the researchers suggest common links [619] . Our work has addressed, in part, this question: ''Can we quantify the chaos observed in the genome from the increasingly available transcriptional data and relate it to tumour progression?'' If no commonalities were observed, we would not have found interesting biomarkers that seem that strongly correlate with the divergences from normal tissue types. We know from our results that these commonalities do occur.

We need to go back to basics to explain these evolving concepts and resolve this apparent paradox. The phrase ''karyotypic entropy'' has been used in the past to define what is actually a divergence from the normal chromosome structure and it genomic organization. This denomination has also been employed by several authors, notably [601] , but it has also been used in at least two other publications [620, 621] . These works have in common the use of this term to refer to a ''disorder'', fuelled by the undergraduate textbooks indoctrination of associating increase of entropy in natural spontaneous processes with the increase of ''observed disorder'' in the system. We propose that the use of a natural measure of divergence, the Jensen-Shannon divergence, could not only be a more formal, but also more appropriate modelling approach. As such, we propose to introduce the term 'karyotypic divergence' or 'karyotypic Jensen-Shannon divergence' to replace this concept and to avoid a subjective approach.

Why is it the case that we observe the Normalized Shannon Entropy of the transcriptional profile decreasing with cancer progression when intuitively our average expected surprisal (Shannon Entropy) should increase with progression?

Arieh Ben-Naim in his recent book ''A farewell to Entropy: Statistical Thermodynamics based on Information'' [622] comments:''It is interesting to note that Landsberg (1978) not only contended that disorder is an ill-defined concept, but actually made the assertion that 'it is reasonable to expect 'disorder' to be an intensive variable'''. Ben-Naim also states: ''In my view, it does not make any difference if you refer to information or to disorder, as subjective or objective. What matters is that order and disorder are not well-defined scientific concepts. On the other hand, information is a well-defined scientific quantity, as much as a point or a line are scientific in geometry, or mass or charge of a particle are scientific in physics.'' However, in a manuscript entitled ''Can Entropy and 'order' increase together ?'' Landberg defines (in an attempt to decouple the notions of order and entropy), for a thermodynamical system that can be on N states the 'disorder' D(N) to be the Normalized Entropy (which is a function of N) divided by Boltzmann's constant [623] . 'Disorder' then is an intensive magnitude bounded by 0 and 1, and 'order' is defined as 1-D(N) .

While Landberg's decoupling argument between order and entropy [623] may still be controversial in Physics, the question is pertinent for our apparent paradox (the question that motivates this subsection). Borrowing from the title of his paper we could now state the central question as ''Can Shannon Entropy increase while the Normalized Shannon Entropy decrease?'' The solution of this apparent paradox is a trick of escapologism, perhaps also paralleled by what a cancer cell may be experiencing (or ''reacting'' in response to increased sources of stresses), and it is worth discussing in this context. Let H[X] be Shannon Entropy for an ensamble X with N different values. We will now assume, and here is the trick, that N is not a constant, but a function of time N(t). Let D(X (N(t) )) be the Normalized Shannon Entropy. By definition D(X(N(t))) = H(X(N(t)))/log 2 (N(t)). Then, just by taking the time derivatives it can be shown that the time variation of D(X(N(t))) can be negative, although the time rate of H[X] can be positive.

where k is a constant. The escape to our paradox is ''achieved'' via making explicit the time variability of N(t). Landberg explicitly mentions that biological systems are examples where growth processes increase N(t), and perhaps the increased diversity in the transcriptome of a cancer cell during progression is one of such examples. This discussion somehow resolves the apparent disassociations due to language barriers that may exist between the different disciplines (physics, information theory, molecular biology and oncology). A biologist may regard a cancer cell as an entity that, during progression, may ''spread'' its transcriptomic profile, including the generation of a large number of novel molecular species (due to adquired characteristics during its ''devolution'' from the normal type). In our informational perspective, this would be analogous to a situation in which the sender of a message, after some time, decides to increase the size of the alphabet of transmitted symbols. Clearly, it is intuitive to think that the receiver would be in a situation of increased Shannon Entropy. However, if the receiver is not aware of the new symbols (or is not able to detect them) and some of the symbols of the previous alphabet are no longer used, the receiver would now perceive a reduction of Normalized Shannon Entropy, observing an increasing order.

We now borrow an illustrative example from Landberg [623] , but we add a twist to this argument for the purpose of illustrating this discussion. Suppose we have a sender transmitting only two possible symbols (N = 2), and we will assume that we have the same probability, let's denote this as (1/2, 1/2). Then the average expected surprisal (Shannon Entropy), is H(X) = 1, and the Normalized Shannon Entropy is also equal to one. Assume now that now our sender starts to transmit using another symbol, so that we now have theoretical probabilities of (0.5, 0.25, 0.25). Then N = 3, and the average expected surprisal increases to H(X9) = 1.5 the Normalized Shannon Entropy is now 1.5/log 2 (3) = 0.946… (a reduction). This 'third symbol' could actually represent a new ''molecular species'' or a protein isoform that would not be normally expressed in that tissue type [624] , or even something entirely new, product of a mutational/deletional event. If our hypothetical high-throughput technology can only be detecting the first two symbols, and following the conventions we established in the 'Materials and Methods' section, we would be ''observing'' frequencies of (2/3,1/ 3) since the other events would not be detected with our equipment. As a consequence, the both the log 2 (2) = 1, Shannon Entropy and the Normalized Shannon Entropy are both reduced to 0.918293. Obviously, we can not count what we can not observe. As a consequence, a degenerating transcriptional profile that produces novel molecular species, and at the same time reduces those which we can not measure with a particular technology, would look increasingly more ordered.

We envision that physicists may find here a fertile ground to explore new ideas and attempt novel mathematical formalisms for cancer progression from the realm of finite-state thermodynamics [625] and in particular endorevesible processes [626] and endoreversible thermodynamics [627] . Some molecular alterations would then be part of the set of revesible processes that could occur in a cancer cell, while other processes like aneuploidy or gene fusions could be truly ''irreversible genetic switches'' associated with cancer progression [628] . If we assume that the process is slow (i.e. the times required for significant variations of the transcriptome's profile is large in comparison with the cell's processes time scales), and follwing the results of Spirkl and Reis [626] , it may be possible that we have a constant entropy production rate exists during cancer progression leading to Hauptmann's ''entropic devolution'' [629] . Hauptmann sees a malignant tumour as ''a dissipative structure arising within the thermodynamical open system of the human body'' that starts when ''a localized surplus of energy exists and there is no possibility to export entropy. An energetic overload in most malignant cells is indicated by their abnormally high phosphorylation state.'' His perspective, preceeded in part by Dimitrov [630] , Klimek [631, 632] and Marinescu and Viculetz [633] might then fit well an endoreversible thermodynamic formalism. Hauptmann says in [629] ''I believe that cancer is a special kind of adaptation to energetic overload, characterized by multiplication and mutation of genomic DNA (generation of new biomolecules which enhance the probability of survival under harmful conditions), and by chiral alterations (reduction of entropy by entrapping energy) leading to abnormal configurated biomolecules. In this regard the genetic alterations are probably secondary changes. Cancer serves to dissipate energy in a type of developmental process but one in which the results are harmful to the whole organism: an entropic devolution.'' This thermodynamical perspective is now worth exploring and we will discuss it in this context. Assuming that a cancer cell is in a state of ''energy overload'', without ''the possibility of exporting entropy'', could it lead to some type of ''genetic alterations''? Which key mechanisms might be impaired? What consequences is this ''system'' delivering? Could this be another hallmark for oncosystems indentification?

In 1871, in this book called ''Theory of Heat'', Maxwell speculated the idea of ''a being, who can see the individual molecules'' and who has enough reactive intelligence to open and close a unique small hole existing between two communicating vessels (called 'A' and 'B'). An ideal gas filled both vessels, so that starting at uniform temperature the intelligent being could observe the molecules and close and open the hole accordingly to a mission: ''to allow only the swifter molecules to pass from A to B, and only the slower ones pass from B to A.'' The being, ''without expenditure of work raise the temperature of B and lower that of A in contradiction to the second law of thermodynamics.'' The ability of the ''being'' to use observable information about the system to lower the thermodynamical entropy has motivated many articles in physics and fuelled the imagination of many since it was originally introduced by Mawell, and named as ''demon'' by Thomson three years later [622] . An excellent collection of articles until 1990 [634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644] was edited by Leff and Rex [645] . The Maxwell ''demon'', far from being ''exorcised'' from Physics, still inspires interesting new perspectives [634,635,636,637,638,639,640,641,642,643,644,646,647 ].

In a letter to Peter Guthrie Tait, Maxwell writes about the ''demons'': ''Is the production of an inequality of temperature their only occupation? No, for less intelligent demons can produce a difference in pressure as well as temperature by merely allowing all particles going in one direction while stopping all those going the other way. This reduces the demon to a valve. As such value him. Call him no more a demon but a valve like that of the hydraulic ram, suppose.'' (from [645] , p. 6). Maxwell gives again here a sign of his brilliant mind, ''degrading'' the demon to a valve, but also offering an inspiring perspective to oncosystems research. Which types of mechanisms exist in biological systems, and particularly in individual cells, to control these differential values in key parameters? Could changes of key physical parameters for metabolic processes of the cytoplasm and cell's organelles like temperature, volume, pH or electrochemical potentials be also implicated in cancer progression?

The influence of temperature may be giving an interesting working hypothesis for further research. What are the consequences if cancer cells are a different type of open system which also operates at a different temperature than a normal cell? Butler et al. have studied p53 and they argue that at temperatures above 37 degrees centigrades wild-type p53 spontaneously loses DNA binding activity. While folding kinetics do not show important changes in a range from 5 to 35 degrees C, the unfolding rates accelerate 10,000-fold. This leads to a somewhat unexpected mechanism of p53 inactivation. It could be the case that a fraction of p53 molecules become trapped in misfolded conformations with each folding-unfolding cycle due to the increased frequency of cycling. The occurrence of misfolded p53 proteins can lead to aggregation and subsequent ubiquitination in the cell, leading to p53 inactivation [648, 649] . If a key ''guardian of the genome integrity'' [650, 651] and its remarkable conformational flexibility [652] is challenged by an increase of temperature [653] , its role in genotoxic damage and adaptive response (like that of the skin to UVB damage [654] ) may be impaired. The same may occur for other members of the DNA damage response. An increment in temperature has already been linked to skin carcinogenesis. Boukamp et al. report in that [655] ''exposure of immortal human HaCaT skin keratinocytes (possessing UV-type p53 mutations) to 40 degrees C reproducibly resulted in tumorigenic conversion and tumorigenicity was stably maintained after recultivation of the tumors.''

On the other hand, natural gradients on physical biochemical properties can also be challenged in a cancer cell. This in turn derives in metabolic processes running under abnormal parametric circumstances. It is well-known that compartimentalization, in biological systems, naturally require the existence of mechanisms that would keep some key state variables relatively constant, or within bounds, for normal operation of the metabolic processes. One example is very illustrative and a case in point. Instead of demons, holes, or valves, the cell requires pores in its membranes to allow osmotic regulatory processes, yet it should preclude the conduction of protons. This is a nanotechnological design problem not faced by Maxwell, but certainly solved by biological systems without the need of an ''intelligent being'' as Mawell cleverly pointed to Tait in his letter.

This discussion brings us to one of the gene families we have already discussed in this paper, the aquaporins [184, 656, 657, 658, 659, 660, 661] . They are considered the primary water channels of cell membranes [662, 663, 664, 665] . The specific functions of each member of this family are now being slowly mapped by several research labs around the world [666] . Their clinical role in cancer [667, 668, 669, 670, 671, 672, 673, 674, 675] ,obesity [676] , malaria [677, 678] and other diseases is emerging [657, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689] . In [690] , our group observed the dowregulation of AQP3 in all melanoma cell lines studied of the NCI-60 dataset of Ross et al.; this dowregulation was also observed for the CNS and Renal cell lines. AQP3 was relatively upregulated for Leukaemia and Colon cell-lines (we refer the reader to the Supplementary Material of [690] for details). Inhibition of AQP3 in prostate cancer cells was already proposed as a mechanism that increases the sensitivity to cryotherapy treatment [691] .

The aquaporins are not ''an intelligent being'' in any real sense, yet they are so formidable selective that they could easily parallel Maxwell demon's efficiency in creating the right conditions for the cell. Wu et al. give us some clues on the role of point mutations in the AQP1 and how their effective electrostatic proton barrier can be impaired [692] . The elicitation of the detailed mechanistic explanation of this extraordinary selectivity is under intense investigation with a number of techniques, including sophisticated molecular dyanamics simulations, for an overview of this field see [665, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708] . One less known feature of aquaporins is that they may not only channel water, but also carbon dioxide and ammonia [709, 710, 711] , glycerol [712] and urea and other small solutes [713] and, very relevant for cancer research, hydrogen peroxide [188] . At least two of members of this family have been observed in the inner mitochondrial membrane in different tissues. This in turn may indicate mitochondrial roles for aquapotins in osmotic swelling induced by apoptotic stimuli [714] .

Could it be possible that we can track cancer progression by looking at some of these ''Maxwell demons''? We have seen in Figure 10 , that AQP3 has a reduced expression with increased progression in our melanoma dataset. Cao et al., reported that ultraviolet radiation induced AQP3 down-regulation in human karatinocytes; thus AQP3 has become a strong and plausible link between UV radiation, skin dehydration [186, 715] and photoaging [189] . This may indicate an impared function on skin hydration [184, 185, 716, 717, 718, 719] . The expression of AQP3, as well as AQP1, AQP5, and AQP9 seem to be correlated with melanoma progression, indicating a common pattern of downregulation from the higher values in normal skin and benign nevi (see Figure 17 ).

Does a similar pattern of aquaporin downregulation exist in prostate cancer? Wang et al. have looked at the expression and localization of AQP3 in human prostate using cell lines as well as patient samples. They have observed AQP3 mRNA ''in both normal and cancerous epithelia of human prostate tissues, but not in the mesenchyme. In the normal epithelia of the prostate, localization was limited to cell membranes, particularly the basolateral membranes. However, the expression of AQP3 protein in the cancer epithelia was not observed on the cell membranes.'' This finding seems to implicate the subcellular localization of AQP3 as a possible indicator of a transition to a more malignant phenotype. Lapointe's dataset allows us to see the downregulation of AQP3 and AQP1. A large subgroup of primary prostate tumors has reduced levels of AQP3 and AQP1 as most of the lymph node metastasis samples [ Figure 18 ].

One critique that we are aware we could receive is that the current manuscript presents a novel methodology and an underlying unifying theory based on retrodictions or postdictions. Indeed we have shown that the use of the Normalized Shannon Entropy and the Information Theory quantifiers (the M-complexities and the Jensen-Shannon divergence) allow to monitor cancer progression and to identify the best biomarkers that correlate with the transcriptomic changes. Our approach works in a retrodiction way in that it looks at data already obtained by other studies, but gives a unifying framework to track cancer progression. For instance, on True et al's dataset, our unifying hallmark of cancer gives not only MAOA, which was already identified in the original publication, but also AMACR, CD40, CDK4, etc. are very important biomarkers for prostate cancer. Analogously, the identification of KLK3/PSA in Lapointe's dataset is another important retrodiction which shows the power of the method.

In some sense our approach also works in a postdiction way, as it helps to evaluate the speculation that cancer cells have ''an entropic devolution''. Our results show that the variations of Normalized Shannon Entropy and Jensen-Shannon divergences indeed give measurable changes, and that these changes are related to important biomarkers in the two types of cancer studied in this work.

In addition, we remark that we are literally making hundreds, or even thousands of predictions. The results in the 'Supplementary Material' provide this information for the detailed scrutiny of our peers. We believe that other probes with gene expression patterns in high correlation with the probes discussed in this paper, and perhaps less studied by immunohistochemistry and other methods in the two cancer types studied here, are worth exploring as a group of biomarkers. These predictions can be tested with further studies on staging and patient stratification.

A very recent study by Ballal et al. have linked BRCA1 to telomere length and maintenance and its loss from the telomere in response to DNA damage [720] (see also [721] ). We have previously mentioned that BRCA1 is a conspiquous biomarker arising from the analysis of True et al.'s dataset using our methods. We found this to correlate with a preivous study that showed that BRCA1 has a reduced expression in immortalized prostate epithelial cells before and after their conversion to tumorigenicity [493] . We also mentioned that the knockdown of BRCA1 leads to anaccumulation of multinucleated cells [492] , preserving chromosomal stability [490] . Ballal et al. telomeric ChIP assays to detect BRCA1 at the telomere and reported time-dependent loss of BRCA1 from the telomere following DNA damage. Due to the role of telomeres in maintaining chromosomal stability [722] and the inverse correlation of telomere length and divergent karyotypes in prostate cancer cell lines [723, 724] (as well as the recognized role of telomere dysfunction in the induction of apoptosis or senescence in vivo [725, 726, 727, 728, 729, 730] , increase of mutation rates [731] , DNA fragmentation [732] , and their relation with DNA damage signalling [733] ), we checked for other probes of genes involved in telomeric function.

From those which we were able to identify in True et al's dataset, we have found a strong high correlation of the expression of BRCA1 with TERF2/TRF2 (telomeric repeat binding factor 2) [734] and a negative correlation with the expression pattern of TERF2IP (telomeric repeat binding factor 2, interacting protein) [ Figure 19 ].

Finally, one particular type of probes has also caught our attention, and we would like to refer to them before concluding this section.

With the denomination of 'non-coding RNA' we identify those RNA molecules which are functional but that are not translated into proteins. Many microarray chips contain probes that are annotated as 'non-protein coding', indicating that there might be some valuable expression data that we can also mine for information. We note that our method, although employing transcriptomic data, does not limit its application to proteincoding information, and that the combined use of protein-coding and non-coding protein probe expression would allow a more comprehensive view of the transcriptional state of the cell.

Among non-protein coding, microRNAs [735] are gaining acceptance as key players in several cancers [736, 737, 738] (including prostate cancer [739, 740] ), but the so-called ''long noncoding RNAs'' [741] are also gaining a place in the scenario of cancer biomarkers (see [742] , and [743, 744, 745] ). We thus turned our attention to these probes that have been annotated as ''nonprotein coding'' and we highlight some of them that have very high correlation values with the Normalized Shannon Entropy in True et al's prostate cancer dataset. In particular, the probes for MALAT1/ MALAT-1 [742, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758] have a very conspiquous position (See Figure 20) . They located very closely to other protein coding biomarkers that have also lost expression and have been discussed in this work like SFPQ, CD40, BRCA1, and TP53 (see Figure 16 ). MALAT1 has been recently pointed as a biomarker in primary human lobular breast cancer as a result of an analysis of over 132,000 Roche 454 highconfidence deep sequencing reads [749] . An international team, searching on thousands of novel non-coding transcripts of the breast cancer transcriptome, has been able to identify more than three hundred reads corresponding to MALAT1 [749] . This is a noncoding RNA which was identified in 2003 in non-small cell lung cancer, was shown to be highly expressed (relative to GAPDH) in lung, pancreas and prostate, but not in other tissues including muscle, skin, stomach, bone marrow, saliva, thyroid and adrenal glands, uterus and fetal liver [758] . MALAT-1, also known as NEAT2, is considered to be ''extraordinarily conseved for a noncoding RNA, more so than even XIST'' [754] . Our results indicate that the reduction of expression of some non-coding RNAs, in particular of MALAT-1, and SNORA60 with respect to their normal expression in prostate, as well as the upregulation of SNHG8 and SNHG1 should be monitored as useful biomarkers to track disease progression.

We will now address another non-coding RNA called NEAT1 which, like NEAT2, is also conserved in the mammalian lingeage. Before we move onto NEAT1, we will first recall a previous result. We have noted before the conspiquous position of SFPQ/PSF (Polypyrimidine tract-binding protein-associated splicing factor) in Figure 16 . The expression of a probe for SPQF has the highest correlation with the values of the Normalized Shannon Entropy. We highlighted before that SFPQ/PSF is a putative regulator of growth factor-stimulated gene expression [498] . The loss of SFPQ expression during the progression of prostate cancer may be an important key to understand this disease or one of its subtypes. We have also mentioned that the AR/PSF complex interacts with the PSA gene (perhaps the most well-established prostate cancer biomarker) and that SFPQ/PSF inhibits AR transcriptional activity [499] . Kuwahara et al. showed that SFPQ together with NONO (Non-POU-domain-containing, octamer binding protein) and PSPC1 (Paraspeckle protein 1 alpha isoform, formerly known as PSP1) are expressed in mouse Sertoli cells of the testis and form complexes that function as coregulators of androgen receptormediated transcription [500] . While new research results [759] link SFPQ and NONO/P54NRB with the RAD51 family of proteins (largely regarded as another key protector of chromosome integrity as being involved in homologous recombination DNA repair), it is perhaps SFPQ and NONO's co-localization in paraspeckles that make this group also remarkable [760] .

Paraspeckles [760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777] are a novel nuclear compartment, of approximately 0.2-1 mm in size, discovered in 2002, by Fox et al. in Dundee Scotland, following the identification of the protein PSPC1 (AF448795) in the nucleolar proteomics project at Lamond's lab which is described well by Fox et al. [777] . Three years later, Fox, Bond and Lamond showed that NONO and PSPC1 form a heterodimer that localizes to paraspeckles in an RNA-dependent manner [773] . Paraspeckles are dynamic structures, observed in numbers that vary between 10 and 20, that seem to control gene expression via retention of RNA in the nucleus [772] . A long noncoding RNA called NEAT1/MEN epsilon/beta [754, 760, 762, 764, 778] , that colocalizes with paraspeckles, seems to be integral to their structure. Depletion of NEAT1 erradicates paraspeckles and a biochemical analysis by Clemson et al indicates that the NEAT1 binds with paraspeckle proteins SFPQ/PSF, P54NRB/NONO and PSPC1. NEAT1 is also known as TncRNA (trophoblast-derived noncoding RNA) [754, 779, 780, 781, 782, 783, 784, 785, 786] and probes for TncRNA exist on this dataset, We have observed in True et al.'s dataset that there exists a high correlation between the Normalized Shannon Entropy with the expression of SFPQ/PSF, P54NRB/NONO, and TncRNA. Overall, this implies that the disruption of the function of the paraspeckles is correlated with the increasing signs of deterioration of normal transcriptomic state of the cells. While a causal relationship still needs to be proved, we admire the mathematical elegance of the Normalized Shannon Entropy of the samples, a global measure of the average expected surprisal of the transcriptome, which in turn has lead us to consider the dysfunction of the smallest nuclear body as a putative biomarker of disease progression. The role of SFPQ/PSF in the control of tumorigenesis is under investigation [787] and the information coming from these studies would need to be integrated with their role, together with P54NRB/NONO and TncRNA, in paraspeckles if we want to achieve a better understanding of these mechanisms.

In this contribution we have shown that for the melanoma and prostate cancer datasets studied, the quantitative changes of Information Theory measures, Normalized Shannon Entropy, Jensen- Figure 19 . The stacked average gene expression of probes corresponding to BRCA1 and TERF2 (telomeric repeat binding factor 2) in True et al's prostate cancer dataset. The first group of samples (1 to 9 in green) correspond to Gleason 3 pattern, indicating that most of the samples in this group have no significantly reduced expression of this pair of genes. The second group of columns (10 to 21 in yellow) correspond to Gleason 4 patterns and the last 8 columns (22 to 29 in red) correspond to Gleason 5 samples. A very recent study by Ballal et al. have linked BRCA1, to telomere length and maintenance and its loss from the telomere in response to DNA damage [720] (see also [721] ). There is an increasing trend of dowregulation, so it would be interesting to evaluate if indeed this pair of proteins could be an early marker of dowregulation useful to evaluate samples with Gleason pattern 2, or if may constitute a biomarker useful to distinguish a prostate cancer subtype. doi:10.1371/journal.pone.0012262.g019

Shannon divergence and the novel Statistical Complexity quantifiers defined here are in high correlation with gene expression changes of well-established biomarkers associated to cancer progression. In addition, variations of the basic technique (i.e. a modified form of statistical complexity) which allows us to better understand the phenotypic changes observed in these samples which are associated with the progression and the transitions of the gene expression profiles. For instance, in a properly defined Statistical Complexity vs. Entropy plane, on a melanoma dataset first studied in Ref. [110] , samples appear in well differentiated ''clusters''. These clusters correlate well with the phonotypic characteristics of normal skin, nevi, primary and metastatic melanoma. In this ''Complexity vs. Entropy'' plane, primary melanomas samples appear ''bridging'' benign nevi and metastatic melanoma samples. Our results may also suggest that the evolution of metastatic melanoma leads to at least two different subtypes.

The Normalized Shannon Entropy of a transcriptional sample profile is calculated associating the measured expression values of a gene with the relatively probability of being expressed. We have observed that, in general, the transcriptomes of tumour progressing cells tend to have lower values of Normalized Shannon Entropy than normal ones. Given a population of normal cells of a given tissue type it is then possible to compute useful measure of divergence of cancer cell profiles from the normal expression average profile, in terms of Information Theory quantifiers, the Shannon Eveness normalized entropy and generalized statistical complexity [788, 789, 790] .

In addition, our observation of the correlation of the statistical complexity of tumours with its natural progression allows an unprecedented way of finding biomarkers that links with the gradual deterioration of the genome integrity. The proposed methodology uncovered, for the first time, evidence of the putative role of impared centrosome cohesion in melanoma progression. Statistical complexity has then been able to pinpoint otherwise unrecognized biomarkers in concert with existing ones, reinforcing the view that ''chromosomal chaos'' and ''cancer as a chromosomal disease'' can be a useful guiding principle to understand the molecular biology of cancer and uncover the timeline of its progression. This is a powerful method to uncover ''oncosystems'' instead of ''oncogenes''. ''Oncosystems'' are a highly differentially disregulated set of genes that, if linked with the molecular ''hallmarks of cancer'' described in the introduction, and existing databases with putative common functional genomic annotations, can help to understand the biological progression pathways that drive the disease.

On one of the prostate cancer dataset studied (obtained from a previous published study, [44] ), we observe a gradual pattern of reduction of Normalized Shannon Entropy from three well characterized tissue types: normal prostate, primary prostate tumours and lymph node metastases. On a different dataset on prostate cancer (from Ref [332] ), we observe that a group of samples having Gleason Figure 16 is that we have now highlighted the position of s ome probes which have been annotated as corresponding to ''non-coding RNAs''. In particular, we highlight those of MALAT1 (Metastasis associated lung adenocarcinoma transcript 1, (non-protein coding)), SNORA60 (small nucleolar RNA, H/ACA box 60); both increasingly downregulated, SNHG1 (small nucleolar RNA host gene 1 (non-protein coding)) and SNHG8 (small nucleolar RNA host gene 8 (non-protein coding)). The probes for MALAT1/MALAT-1 [742, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758] have a very conspiquous position, which we could judge a priori to be equivalent in relevance to those of the previously discussed roles of SFPQ, CD40, BRCA1, and TP53 (see Figure 16 ). MALAT1 has been recently pointed as a biomarker in primary human lobular breast cancer as a result of an analysis of over 132,000 Roche 454 high-confidence deep sequencing reads. Within the thousands of novel non-coding transcripts of the breast cancer transcriptome, Guffanti al., identified more than three hundred reads corresponding to MALAT1 [749] . This non-coding RNA, first identified in 2003 in non-small cell lung cancer, was shown to be highly expressed (relative to GAPDH) in lung, pancreas and prostate, but not in other tissues including muscle, skin, stomach, bone marrow, saliva, thyroid and adrenal glands, uterus and fetal liver (see figure four of Ref. [758] ). Our results indicate that the reduction of expression of some non-coding RNAs, in particular of MALAT-1, and SNORA60 with respect to their normal expression in prostate, as well as the upregulation of SNHG8 and SNHG1 should be monitored as useful biomarkers to track disease staging and progression to a more malignant phenotype. Interestingly enough, a study published in 2006 by Nadminty et al. has shown that KLK3/PSA modulates several genes, reporting a 16.5 fold downregulation of MALAT1 [810] . While these results have been obtained using the human osteosarcoma cell line SaOS-2, our results indicate that MALAT1 expression in the normal prostate and in cancer cells could also be considered as a relevant biomarkers to be tested in the future. doi:10.1371/journal.pone.0012262.g020 patterns 4 and 5 (two patterns which are typically associated to an aggressive phenotype) have lower Normalized Shannon Entropy values than a subset of Gleason pattern 3 (a pattern which is normally associated to a less aggressive phenotype but which nevertheless is still of clinical concern). However, a group of samples having Gleason patterns 3, 4, and 5 is revealed; this mixed cluster has a mid-range entropy. This is an interesting fact which correlates with the limitations observed in Ref. [332] . We note the authors' comment: ''We were unable to identify a cohort of genes that could distinguish between pattern 4 and 5 cancers with sufficiently high accuracy to be useful, suggesting a high degree of similarity between these cancer histologies or substantial molecular heterogeneity in one or both of these groups.'' Our results provide a conciliatory middle ground that explains the perceived clinical usefulness of Gleason pattern classification, widely used around the world, while at the same time reveals the reason for the difficulties of obtaining a good transcriptional signature for the other two patterns [791] .

We have seen, through a detailed discussion of several biomarkers in three different datasets, that the variation of the gene expression distributional profile can be characterized via Information Theory quantifiers. Our study also showed that current established biomarkers of the two diseases studied seem to correlate with those that best co-variate with these quantifiers. For instance, AMACR, in our second prostate cancer dataset studied, naturally appears as one of the most correlated genes (in both the Pearson and the Spearman sense) with the pattern of variation of Entropy of the samples. Together with MAOA, which is the highlighted gene in True et al.'s [332] original publication, AMACR is now being recognized as one of the best biomarkers in primary prostate cancer with approximately 180 publications dedicated to it in the past five years. We have also shown that many gene probes that best correlate with the divergence of the normal tissue profile have been identified as useful biomarkers (via other accepted validation methods). This said, the use of other sources of information, like pathway or gene ontology databases has lead as to the identification of other cell processes that may be altered.

We have presented a unifying hallmark of cancer, the cancer cell's transcriptome changes its Normalized Shannon Entropy (as measured by high-througput technologies), while it increments its physical Entropy (via creation of states we might not measure with our devices). This hallmark allows, via the use of the Jensen-Shannon divergence, to identify the arrow of time of the process, and helps to map the phenotypical and molecular hallmarks of cancer as major converging trends of the transcriptome. The methodology has produced remarkable postdictions and retrodictions that show that it can predictively guide biomarker discovery.

We refer the reader to the original publications for details of methods for data collection, but we highlight here some aspects that are important to understand the data generation process for the purpose of our analysis.

Samples were obtrained from radical prostatectomy surgical procedures. Samples are labelled as ''tumors'' if they contain at least 90% of cancerous epithelial cells, and they were considered as ''non-tumor'' if they contain no tumor epithelium and are from the noncancerous region of the prostate. The later samples were labelled ''normals'' although the authors alert that some may contain dysplasia. In this dataset, Lapointe et al. have performed a gene expression profiling by using cDNA microarrays containing 26,260 different human genes (UniGene clusters). Using 50 mg of total RNA from prostate samples Cy5-labeled cDNA was prepared and Cy3-labeled cDNA used 1.5 mg of mRNA common reference, pooled from 11 human cell lines (see Ref. [792] ). The fluorescence ratios were subsequently normalized by mean centering genes for each array, a relatively standard procedure. In addition, to minimize potential print run specific bias, Lapointe et al. report that ratios were then mean centered for each gene across all arrays according to Ref. [793] . We have only used the genes that the authors report in their first figure, 5,153 genes that have been well measured and have significan variation in some of the samples. For the other details of their matrials and methods we refer the readers to the Supporting Notes and the Materials and Methods section of their original publication [44] .

Samples were obtained from nevus volunteers and melanoma patients and only those samples that have more than 90% of tumor cells were profiled. The 20,862 cDNAs used (Research Genetics, Huntsville, AL) represent 19,740 independent loci. (Unigene build 166).median of ratio values from the experiment were subjected to linear normalization in nomad (which can be accessed at http://derisilab.ucsf.edu), log-transformed (base 2), and filtered for genes where data were present in 80% of experiments, and where the absolute value of at least one measurement was .1.

In this dataset, samples have information of 15,488 spots per array, with a total of 7,700 unique cDNAs represented. The samples were obtained from frozen tissue blocks from 29 radical prostatectomies accessioned and selected to represent Gleason grades 3, 4, and 5. The samples are ''treatment naïve'', meaning that they were also selected such that their gene expression profile is also and the absence of any bias that the treatment before prostatectomy. The frozen sections (8 mm) were cut from optimal cutting temperature medium blocks and immediately fixed in cold 95% ethanol. Around 5,000 epithelial cells from both histologically benign glands and cancer glands were separately laser-capture microdissected (LCM). The authors of the study have also been very careful to include only one Gleason pattern in each laser-captured cancer sample, following a process in which the patterns were assessed independently by two investigators.The matched benign epithelium was captured for each cancer sample for a total of 121 samples.

An important characteristic of this dataset is the normalization procedure. For each spot and in each channel (Cy3 and Cy5), True et al. substracted the median background intensity from the median foreground intensity, and subsequently the log ratios of cancer expression to benign expression were computed. These ratios were obtained by first dividing the background-subtracted intensities (Prostate Cancer/Benign) and then taking the logarithm base 2. In the case that the median background intensity was greater than the median foreground intensity, the spot was considered missing. We refer to the original publication for the other aspects of imputation, spot quality and filtering, but, like in Lapointe et al's study, they also filter to keep informative (expression ratios of benign versus cancer should at least be 1.5-fold or greater in at least half of one of the Gleason groups as one of the selection criteria).

Shannon Entropy. In many circumstances, experimental measurements are associated with the accumulation of individual results which, ultimately, qualitatively and quantitatively characterized our experimental observations. The presence (or absence) of a particular result of an individual experimental measure is called an event. An event which can take one of several possible values is called a random variable. Analogously, a random event is an event that can either fail to happen, or happens, as a result of an experiment. An event is certain if it can not fail to happen and it is said to be impossible if it can never happen.

Following Andreyev [794] , we will define the probability p(x) of an event x, as the theoretical frequency of the event x about which the actual frequency occurrence of the event shows a tendency to fluctuate as the experiment is repeated many times. The Shannon information content of an event x (or the surprisal of an event x, [795] ), is defined as h(x)~log 2 1 p(x)

Following McKay [796] , an ensamble X is a triple (x,A X ,P X ), where x is the value of a random variable, which takes on one of a set of possible values, A X~f a 1 ,a 2 ,:::,a i ,:::,a N g, having probabilities P X~f p 1 ,p 2 ,:::,p N g, with p(x~a i )~p i , p i §0 and X a i [A X p(x~a i )~1.

The Shannon Entropy of an ensemble X (also known as the uncertainty of X), denoted as H[X], is defined to be the average Shannon information content. It is the average expected surprisal for an infinitely long series of experiments. We use the theoretical frequencies to compute this average, and then we have

Suppose that we have a fair dice, the theoretical frequency of an event 'the dice shows a three' is 1/6, (if the dice is assumed fair, the theoretical frequency is the same for any number from 1 to 6). In that case a hypothetical experimentalist guessing will have an average expected surprise of H[X] = log 2 (6) . We note the two natural bounds that the entropy can have. The Shannon Entropy of an ensemble X is always greater or equal to zero. It can only be zero if p(x~a i )~1 for only one of the N elements of A X~f a 1 ,a 2 ,:::,a i ,:::,a N g. On the other hand, the Shannon Entropy is maximized in the case that p(x~a i )~1=N. This is the so-called ''equiprobable distribution'', a uniform probability distribution over the finite set. Transcriptional Shannon Entropy. Let f (j) i the expression value of probe i (i = 1,…, N) on sample j (j = 1, …, M). For each sample j we first normalize the expression values. We interpret them as the theoretical frequency of a single hybridization event. We then define a probability distribution function (PDF) over a finite set as: The uniform (equiprobably) distribution is defined as i Let H e~H ½P e ~log 2 N, then in this paper we always use the Normalized Shannon Entropy, defined as:

i H e , j~1, . . . ,M

The Jensen-Shannon divergence and the Statistical complexity measures

Given a probability distribution function over a discrete finite set, is then straightforward to calculate its Normalized Shannon Entropy if we have the theoretical frequencies. Several measures of ''complexity'' of a probability distribution function have been proposed. In this work we have used Statistical Complexity measures.

All the complexity measures used in this work are the product of a Normalized Shannon Entropy of the probability distribution function, and a divergence measure to a reference probability distribution function. We follow earlier proposals by López-Ruiz, Mancini and Calbet who first introduced a statistical complexity measure based on such a product in [797] . The LMC-Statistical Complexity is the product of the Normalized Shannon Entropy, H[P], times the disequilibrium, Q[P]; the latter given by the Euclidean distance from P to P e , the uniform probability distribution over the ensemble. In this paper we used a later modification which we refer as the MPR-Statistical Complexity [43] which replaces the Euclidean distance between P to P e by the Jensen-Shannon divergence [788, 798] . The Jensen-Shannon divergence is linked in physics to the thermodynamic length [799, 800, 801, 802] .

We define the MPR-Statistical complexity [790] as:

where Q P (j) ,P e  à Q 0 J s P (j) ,P e  à , Q 0 is a normalization factor, and J s P (1) ,P (2)  à is the Jensen-Shannon's divergence between two probability density functions P (1) and P (2) , which in turn is defined as J s P (1) ,P (2)  à H P (1) zP (2) 2

In this work, in many cases we compute the Jensen-Shannon divergences of a probability with a probability of reference which is not the uniform probability distribution over the ensemble. In general, it is the average over a subset of probability distribution functions which are consider to be either the ''initial'' of ''final'' states of interest. Let P ave be such an average, then the M-Statistical Complexity of a probability distribution function P (j) , given a P ave of reference, is given by C (M) P (j)  Ã~H P (j)  à : J s P (j) ,P ave  à An illutrative example. In order to discuss a relatively simple example that can intuitively provide a grasp of the basic mathematical principles of Information Theory we present a hypothetical ''gene expression'' dataset involving four samples each with the expression of five unique probes corresponding to five genes (not necessarily different) as follows in Table 3 .

One of the quantifiers that we use in this contribution describes a measure of order for a sample: the Normalized Shannon Entropy also known as Shannon Evenness Index [803] . This section focuses on this quantifiers use and importance (refer to the 'Materials and Methods' section to see how this measure is calculated). In Sample 4 all probes have the same expression therefore it has the highest achievable value of Normalized Shannon Entropy (H = 1). The Normalized Shannon Entropy values for samples 1 and 2 are the same (H = 0.82). Sample 3, which tends to be less peaked and has the two most significantly expressed genes with the same value, has a higher value of Normalized Shannon Entropy (H = 0.92) (see Figure 21 ).

This simple example shows that the Normalized Shannon Entropy variations of the gene expression profile convey information about global transcriptomic changes; however, this measure alone is not enough to characterize the deviations from normal tissue profiles. For example, assume that Sample 1 is the normal profile of a particular tissue type. Assume that Sample 3 is the profile of a cancer cell that originated from that tissue type, the variation of Normalized Shannon Entropy can be related to this malignant change. However, as Sample 2 illustrates, Normalized Shannon Entropy is not enough to let us to measure the variation from a profile and at least another Information Theory quantifier is needed. We resort to Statistical Complexity quantifiers, which in turn use the Jensen-Shannon divergence [798] to provide this complementary dimension [800] (refer to the 'Materials and Methods' section for a mathematical definition of the Jensen-Shannon divergence). Figure 21 shows how the Jensen-Shannon divergence helps us to evaluate the variation between profiles. Samples 1 and 2, as perhaps intuitively expected, have the largest divergence between them, their Jensen Shannon divergence is 0.286636 (JS(1,2) = Table 3 . An example dataset to illustrate the principles of Shannon Entropy and the Information Theory quantifiers used in this work. Table 3 . Sample 4 has the largest attainable value since the expression of all probes is the same. Samples 1 and 2, which have the same set of expression values, although in different probes, have the same value of Normalized Shannon Entropy. As a consequence, there is a need for another quantifier of gene expression to address the permutational indistinguishability of these two expression profiles. The Jensen-Shannon divergence provides a natural alternative (see Table 4 ). doi:10.1371/journal.pone.0012262.g021 Table 4 . Jensen-Shannon divergence values using the example introduced in Table 3 . Table 4 . Let H P (j) Â Ã be the Normalized Shannon Entropy of a transcriptional sample profile, then the MPR-Statistical Complexity C (MPR) P (j) Â Ã is defined as being proportional to the product of the Normalized Shannon Entropy times the Jensen-Shannon divergence of the profile with the equiprobable distribution (in the example above the equiprobable distribution is that of Sample 4). Then we have

Where Q 0 is a normalization factor. Once again, we refer to the 'Materials and Methods' sections for the accompanying formal mathematical presentation. As a consequence, we can plot the MPR-Statistical Complexity of the samples of our example as a function of the Normalized Shannon Entropy as can be seen in Figure 22 . Annotated genes. A full list of gene references in this paper along with their descriptions from iHOP (http://www.ihop-net. org/UniPub/iHOP/) can be found in supplementary material reference File S5. 

Stemness, cancer and cancer stem cells

Stemness'' genomics law governs clinical behavior of human cancer: implications for decision making in disease management

An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors

Mitochondrial localization, ELK-1 transcriptional regulation and growth inhibitory functions of BRCA1, BRCA1a, and BRCA1b proteins

Mitochondria and cancer

Glucose avidity of carcinomas

Mitochondrial DNA instability and metabolic shift in human cancers

Roles of p53, MYC and HIF-1 in regulating glycolysis -the seventh hallmark of cancer

Alteration of the bioenergetic phenotype of mitochondria is a hallmark of breast, gastric, lung and oesophageal cancer

Deregulation of the G1/S-phase control in human testicular germ cell tumours

Aberrant DNA methylation as a cancer-inducing mechanism

Promoter hypermethylation of hallmark cancer genes in atypical adenomatous hyperplasia of the lung

Loss of acetylation at Lys16 and trimethylation at Lys20 of histone H4 is a common hallmark of human cancer

Metabolic transformation in cancer

Metabolic targeting of cancers: from molecular mechanisms to therapeutic strategies

Tumor cell metabolism: cancer's Achilles' heel

Role of hypoxia in the hallmarks of human cancer

Adaptation to hypoxia and acidosis in carcinogenesis and tumor progression

MicroRNAs: Novel regulators in the hallmarks of human cancer

MicroRNAs and the hallmarks of cancer

Genetic and epigenetic mosaicism in cancer precursor tissues

Road to the crossroads of life and death: Linking sister chromatid cohesion and separation to aneuploidy, apoptosis and cancer

Chromosome aberrations in solid tumors

Instability of chromosome structure in cancer cells increases exponentially with degrees of aneuploidy

Genetic instability of cancer cells is proportional to their degree of aneuploidy

Cell junctions and the biological behaviour of cancer

Mitochondrial gateways to cancer

Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability

Pathways connecting inflammation and cancer

Hallmarks for senescence in carcinogenesis: novel signaling players

NF-kappaB and cancer-identifying targets and mechanisms

Altered growth pattern, not altered growth per se, is the hallmark of early lesions preceding cancer development

MicroRNAs and cancer epigenetics

Common themes of dedifferentiation in somatic cell reprogramming and cancer

Disruption of differentiation in human cancer: AML shows the way

Molecular theory of cancer

Principles of cancer therapy: oncogene and non-oncogene addiction

Stem cell regulation and the development of blast crisis in chronic myeloid leukemia: Implications for the outcome of Imatinib treatment and discontinuation

Classification of prostatic carcinomas

Trends in reporting Gleason score 1991 to 2001: changes in the pathologist's practice

A comparison of interobserver reproducibility of Gleason grading of prostatic carcinoma in Japan and the United States

Cutaneous melanoma: available therapy for metastatic disease

Gene expression profiling identifies clinically relevant subtypes of prostate cancer

Revisiting the relation between species diversity and information theory

Differential post-transcriptional regulation of two Ink4 proteins, p18(Ink4c) and p19(Ink4d)

INK4 proteins, a family of mammalian CDK inhibitors with novel biological functions

Loss of the forkhead transcription factor FoxM1 causes centrosome amplification and mitotic catastrophe

FoxM1 is required for execution of the mitotic programme and chromosome stability

The role of the BRCA2 gene in susceptibility to prostate cancer revisited

Prostate cancer in male BRCA1 and BRCA2 mutation carriers has a more aggressive phenotype

Down-regulation of BRCA2 expression by collagen type I promotes prostate cancer cell proliferation

BRCA2 mutation in a family with hereditary prostate cancer

Loss of BRCA2 promotes prostate cancer cell invasion through up-regulation of matrix metalloproteinase-9

Rapid progression of prostate cancer in men with a BRCA2 mutation

Rare germline mutations in the BRCA2 gene are associated with early-onset prostate cancer

CCL2 induces prostate cancer transendothelial cell migration via activation of the small GTPase Rac

Prostate cancer promotes CD11b positive cells to differentiate into osteoclasts

A destructive cascade mediated by CCL2 facilitates prostate cancer growth in bone

CCL2 is a potent regulator of prostate cancer cell migration and proliferation

CCL2, survivin and autophagy: new links with implications in human cancer

CCL2 protects prostate cancer PC3 cells from autophagic death via phosphatidylinositol 3-kinase/AKT-dependent survivin up-regulation

Co-inoculation of prostate cancer cells with U937 enhances tumor growth and angiogenesis in vivo

PTHrPinduced MCP-1 production by human bone marrow endothelial cells and osteoblasts promotes osteoclast differentiation and prostate cancer cell proliferation and invasion in vitro

Monocyte chemotactic protein-1 mediates prostate cancer-induced bone resorption

CCL2 as an important mediator of prostate cancer growth in vivo through the regulation of macrophage infiltration

Targeting CCL2 with systemic delivery of neutralizing antibodies induces prostate cancer tumor regression in vivo

PAR1-mediated RhoA activation facilitates CCL2-induced chemotaxis in PC-3 cells

Identification of differentially expressed genes in organ-confined prostate cancer by gene expression array

The role of c-Jun and c-Fos expression in androgen-independent prostate cancer

Molecular Cancer Phenotype in Normal Prostate Tissue

Activator protein-1 transcription factors are associated with progression and recurrence of prostate cancer

Raf-1 expression may influence progression to androgen insensitive prostate cancer

A mechanism of AP-1 suppression through interaction of c-Fos with lamin A/C

Fast regulation of AP-1 activity through interaction of lamin A/C, ERK1/2, and c-Fos at the nuclear envelope

The role of Sox9 in prostate development

Sox9 is required for prostate development

Sexual differentiation

Androgen-induced programs for prostate epithelial growth and invasion arise in embryogenesis and are reactivated in cancer

SOX9 is expressed in normal prostate basal cells and regulates androgen receptor expression in prostate cancer cells

Inducible FGFR-1 activation leads to irreversible prostate adenocarcinoma and an epithelial-to-mesenchymal transition

SOX9 is expressed in human fetal prostate epithelium and enhances prostate cancer invasion

Calcification of multipotent prostate tumor endothelium

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

PSA response signatures -a powerful new prognostic indicator after radiation for prostate cancer?

First results of PET/ CT-guided secondary lymph node surgery on patients with a PSA relapse after radical prostatectomy

Vascular endothelial growth factor receptor 1 expression in pelvic lymph nodes predicts the risk of cancer progression after radical prostatectomy

Prostate cancer patients with lymph node metastasis

Radioisotope guided sentinel lymph node dissection in patients with localized prostate cancer: results of the first 100 cases

Risk stratification for positive lymph nodes in prostate cancer

Use of preoperative plasma endoglin for prediction of lymph node metastasis in patients with clinically localized prostate cancer

The rate of lymph node invasion (LNI) in men with PSA values less than 10 ng/ml

Pelvic lymph node irradiation for prostate cancer: who, why, and when?

The role of P501S and PSA in the diagnosis of metastatic adenocarcinoma of the prostate

Detection and management of isolated lymph node recurrence in patients with PSA relapse

Significance of micrometastases in pelvic lymph nodes detected by real-time reverse transcriptase polymerase chain reaction in patients with clinically localized prostate cancer undergoing radical prostatectomy after neoadjuvant hormonal therapy

18F]fluorocholine PET/CT imaging for the detection of recurrent prostate cancer at PSA relapse: experience in 100 consecutive patients

Incidence of positive pelvic lymph nodes in patients with prostate cancer, a prostate-specific antigen (PSA) level of , or = 10 ng/mL and biopsy Gleason score of , or = 6, and their influence on PSA progression-free survival after radical prostatectomy

Histopathological evaluation of radical prostatectomy in the treatment of localized prostate cancer

Disease progression and survival in patients with prostate carcinoma and positive lymph nodes after radical retropubic prostatectomy

Is pelvic lymph node dissection necessary in patients with a serum PSA,10ng/ml undergoing radical prostatectomy for prostate cancer?

Usefulness of the nadir value of serum prostate-specific antigen measured by an ultrasensitive assay as a predictor of biochemical recurrence after radical prostatectomy for clinically localized prostate cancer

Lymphovascular invasion is an independent prognostic factor in prostatic adenocarcinoma

Lymph node staging in prostatic carcinoma revisited

Prostate specific antigen progression in men with lymph node metastases following radical prostatectomy: results of long-term followup

Natural history of disease progression in patients who fail to achieve an undetectable prostate-specific antigen level after undergoing radical prostatectomy

The value of prostate-specific antigen levels in pelvic lymph nodes for diagnosing metastatic spread of prostate cancer

Pelvic lymphadenectomy can be omitted in selected patients with carcinoma of the prostate: development of a system of patient selection

The gene expression signatures of melanoma progression

The p53 family: same response, different signals

P53 family activities in development and cancer: relationship to melanocyte and keratinocyte carcinogenesis

Contribution of p53, p63, and p73 to the developmental diseases and cancer

and p63: why do we still need them?

TP63 gene in stress response and carcinogenesis: a broader role than expected

Properties of the six isoforms of p63: p53-like regulation in response to genotoxic stress and cross talk with DeltaNp73

) miR-203 represses 'stemness' by repressing DeltaNp63

Melanocyte and keratinocyte carcinogenesis: p53 family protein activities and intersecting mRNA expression profiles

Comparative study of p63 and p53 expression in tissue microarrays of malignant melanomas

Connecting p63 to cellular proliferation: the example of the adenosine deaminase target gene

Adenosine deamination sustains dendritic cell activation in inflammation

CK2-site phosphorylation of p53 is induced in DeltaNp63 expressing basal stem cells in UVB irradiated human skin

Adenosine deaminase activity in the serum and malignant tumors of breast cancer: the assessment of isoenzyme ADA1 and ADA2 activities

Adenosine deaminase, xanthine oxidase, superoxide dismutase, glutathione peroxidase activities and malondialdehyde levels in the sera of patients with head and neck carcinoma

Activities of adenosine deaminase and 59-nucleotidase in cancerous and noncancerous human colorectal tissues

Serum adenosine deaminase and arylsulphatase A as an index of early infiltration of central nervous system in acute lymphoblastic leukemia

Serum adenosine deaminase and total superoxide dismutase activities before and after surgical removal of cancerous laryngeal tissue

Dipeptide proline diphenyl phosphonates are potent, irreversible inhibitors of seprase (FAPalpha)

Dipeptidyl peptidase inhibits malignant phenotype of prostate cancer cells by blocking basic fibroblast growth factor signaling pathway

Loss of dipeptidyl peptidase IV immunostaining discriminates malignant melanomas from deep penetrating nevi

A marker for neoplastic progression of human melanocytes is a cell surface ectopeptidase

Expression of CD26/dipeptidyl-peptidase IV in benign and malignant pigment-cell lesions of the skin

Adenosine downregulates DPPIV on HT-29 colon cancer cells by stimulating protein tyrosine phosphatases and reducing ERK1/2 activity via a novel pathway

Adenosine down-regulates the surface expression of dipeptidyl peptidase IV on HT-29 human colorectal carcinoma cells: implications for cancer cell behavior

Differential regulation of centrosome integrity by DNA damage response proteins

Genomic models of metastatic cancer: functional analysis of death-from-cancer signature genes reveals aneuploid, anoikis-resistant, metastasis-enabling phenotype with altered cell cycle control and activated Polycomb Group (PcG) protein chromatin silencing pathway

Expression patterns of polo-like kinase 1 in human gastric cancer

Overexpression of polo-like kinase 1 (PLK1) and chromosomal instability in bladder cancer

Polo-like kinase 1 expression is a prognostic factor in human colon cancer

Polo-like kinase 1 (PLK1) is overexpressed in primary colorectal cancers

Aberrant Polo-like kinase 1-Cdc25A pathway in metastatic hepatocellular carcinoma

Polo-like kinase 1 expression in medullary carcinoma of the thyroid: its relationship with clinicopathological features

Silencing of polo-like kinase (Plk) 1 via siRNA causes inhibition of growth and induction of apoptosis in human esophageal cancer cells

Identification of human polo-like kinase 1 as a potential therapeutic target in pancreatic cancer

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories

Polo-like kinase isoforms in breast cancer: expression patterns and prognostic implications

Expression of polo-like kinase (PLK1) in thin melanomas: a novel marker of metastatic disease

Biodistribution and kinetics of the novel selective oncolytic adenovirus M1 after systemic administration

Confirming the RNAi-mediated mechanism of action of siRNA-based cancer therapeutics in mice

Normal cells, but not cancer cells, survive severe Plk1 depletion

Polo-like kinase (Plk)1 depletion induces apoptosis in cancer cells

Effect of antisense RNA targeting Polo-like kinase 1 on cell growth in A549 lung cancer cells

A panel of isogenic human cancer cells suggests a therapeutic approach for cancers with inactivated p53

Plk1 depletion in nontransformed diploid cells activates the DNA-damage checkpoint

GATHER: a systems approach to interpreting genomic signatures

Profiler-a webbased toolset for functional profiling of gene lists from large-scale experiments

Cryptic splicing at a non-consensus splice-donor in a patient with a novel mutation in the plakophilin-1 gene

Genetic diseases of junctions

Ectodermal dysplasia-skin fragility syndrome resulting from a new homozygous mutation, 888delC, in the desmosomal protein plakophilin 1

Clinical and molecular significance of splice site mutations in the plakophilin 1 gene in patients with ectodermal dysplasiaskin fragility syndrome

Inherited disorders of desmosomes

Hereditary mucoepithelial dysplasia: clinical, ultrastructural and genetic study of eight patients and literature review

Homozygous splice site mutations in PKP1 result in loss of epidermal plakophilin 1 expression and underlie ectodermal dysplasia/skin fragility syndrome in two consanguineous families

Assessment of splice variant-specific functions of desmocollin 1 in the skin

Desmosomes: structure and function in normal and diseased epidermis

Genomic amplification of the human plakophilin 1 gene and detection of a new mutation in ectodermal dysplasia/skin fragility syndrome

Genomic organization and amplification of the human plakoglobin gene (JUP)

Skin fragility and hypohidrotic ectodermal dysplasia resulting from ablation of plakophilin 1

Hereditary diseases of desmosomes

A novel genodermatosis caused by mutations in plakophilin 1, a structural component of desmosomes

Decreased plakophilin-1 expression promotes increased motility in head and neck squamous cell carcinoma cells

The distribution of the desmosomal protein, plakophilin 1, in human skin and skin tumors

Plakoglobin is required for effective intermediate filament anchorage to desmosomes

Plakophilins-hard work in the desmosome, recreation in the nucleus?

The function of plakophilin 1 in desmosome assembly and actin filament organization

Plakophilins: Multifunctional proteins or just regulators of desmosomal adhesion?

Comparative analysis of armadillo family proteins in the regulation of a431 epithelial cell junction assembly, adhesion and migration

Desmoplakin assembly dynamics in four dimensions: multiple phases differentially regulated by intermediate filaments and actin

A role for plakophilin-1 in the initiation of desmosome assembly

Lack of plakophilin 1 increases keratinocyte migration and reduces desmosome stability

The desmoglein-specific cytoplasmic region is intrinsically disordered in solution and interacts with multiple desmosomal protein partners

The fate of desmosomal proteins in apoptotic cells

Aquaporin water channels in mammals

Skin aquaporins: function in hydration, wound healing, and skin epidermis homeostasis

Roles of aquaporin-3 water channels in volume-regulatory water flow in a human epithelial cell line

Osmotic stress up-regulates aquaporin-3 gene expression in cultured human keratinocytes

Increased expression of aquaporin 3 in atopic eczema

Specific aquaporins facilitate the diffusion of hydrogen peroxide across membranes

All-trans retinoic acid attenuates ultraviolet radiation-induced down-regulation of aquaporin-3 and water permeability in human keratinocytes

Reactive oxygen species: a breath of life or death?

Role of claudins in tumorigenesis

Changes in the distribution pattern of Claudin tight junction proteins during the progression of mouse skin tumorigenesis

Claudin-1 is a metastasis suppressor and correlates with clinical outcome in lung adenocarcinoma

Reexpression of the TJ protein CLDN1 induces apoptosis in breast tumor spheroids

Claudin-1, -3 and -4 proteins and mRNA expression in benign and malignant breast lesions: a research study

Decreased expression of claudin-1 correlates with recurrence status in breast cancer

Constitutive activation of Wnt/beta-catenin signaling pathway in migrationactive melanoma cells: role of LEF-1 in melanoma with increased metastatic potential

Osteopontin expression and distribution in human carcinomas

Functional analysis of the osteopontin molecule

Osteopontin activation of c-src in human melanoma cells requires the cytoplasmic domain of the integrin alpha v-subunit

Osteopontin N-terminal domain contains a cryptic adhesive sequence recognized by alpha9beta1 integrin

An examination of the effects of hypoxia, acidosis, and glucose starvation on the expression of metastasis-associated genes in murine tumor cells

Structural requirements for alpha 9 beta 1-mediated adhesion and migration to thrombin-cleaved osteopontin

Chimeric peptides of statherin and osteopontin that bind hydroxyapatite and mediate cell adhesion

Natural metabolites of 1alpha, 25-dihydroxyvitamin D(3) retain biologic activity mediated through the vitamin D receptor

Osteopontin deficiency reduces experimental tumor cell metastasis to bone and soft tissues

Osteopontin stimulates tumor growth and activation of promatrix metalloproteinase-2 through nuclear factor-kappa B-mediated induction of membrane type 1 matrix metalloproteinase in murine melanoma cells

Autocrine stimulation by osteopontin contributes to antiapoptotic signalling of melanocytes in dermal collagen

Cooperative role of osteopontin with type I collagen on the metastasis of murine melanoma cells

Osteopontin induces nuclear factor kappa Bmediated promatrix metalloproteinase-2 activation through I kappa B alpha/ IKK signaling pathways, and curcumin (diferulolylmethane) down-regulates these pathways

Osteopontin-deficiency suppresses growth of B16 melanoma cells implanted in bone and osteoclastogenesis in co-cultures

Nuclear factor-inducing kinase plays a crucial role in osteopontin-induced MAPK/IkappaBalpha kinasedependent nuclear factor kappaB-mediated promatrix metalloproteinase-9 activation

Gene expression analysis to identify mRNA markers of cardiac myxoma

Osteopontin: it's role in regulation of cell motility and nuclear factor kappa B-mediated urokinase type plasminogen activator expression

Osteopontin expression correlates with melanoma invasion

Osteopontin in melanocytic lesions-a first step towards invasion?

Osteopontin expression correlates with melanoma invasion

Osteopontin expression and serum levels in metastatic uveal melanoma: a pilot study

Osteopontin is a downstream effector of the PI3-kinase pathway in melanomas that is inversely correlated with functional PTEN

Alpha-V-dependent outside-in signaling is required for the regulation of CD44 surface expression, MMP-2 secretion, and cell migration by osteopontin in human melanoma cells

Regulation of mesenchymal stem cell and chondrocyte differentiation by MIA

Systematic search for the best gene expression markers for melanoma micrometastasis detection

CD44 and the adhesion of neoplastic cells

Non-RGD domains of osteopontin promote cell adhesion without involving alpha v integrins

Secreted phosphoprotein mRNA is induced during multi-stage carcinogenesis in mouse skin and correlates with the metastatic potential of murine fibroblasts

Osteopontin as a molecular prognostic marker for melanoma

Aurora B-mediated abscission checkpoint protects against tetraploidization

SNS-314, a pan-Aurora kinase inhibitor, shows potent anti-tumor activity and dosing flexibility in vivo

MAP kinase meets mitosis: a role for Raf Kinase Inhibitory Protein in spindle checkpoint regulation

Molecular classification of melanoma using real-time quantitative reverse transcriptasepolymerase chain reaction

The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis

Defective cell cycle checkpoint functions in melanoma are associated with altered patterns of gene expression

Cdc7 expression in melanomas, Spitz tumors and melanocytic nevi

Global analysis of gene expression changes during retinoic acid-induced growth arrest and differentiation of melanoma: comparison to differentially expressed genes in melanocytes vs melanoma

Cell cycle regulation of central spindle assembly

Relocation of Aurora B from centromeres to the central spindle at the metaphase to anaphase transition requires MKlp2

ASAP is a novel substrate of the oncogenic mitotic kinase Aurora-A: phosphorylation on Ser625 is essential to spindle formation and mitosis

ASAP, a human microtubule-associated protein required for bipolar spindle assembly and cytokinesis

Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression

The Nek6 and Nek7 protein kinases are required for robust mitotic spindle formation and cytokinesis

The NIMAfamily kinase Nek6 phosphorylates the kinesin Eg5 at a novel site necessary for mitotic spindle formation

Integrative approach for differentially overexpressed genes in gastric cancer by combining large-scale gene expression profiling and network analysis

Nek6 is involved in G2/M phase cell cycle arrest through DNA damage-induced phosphorylation

Polo-like kinase 1 (Plk1) in non-melanoma skin cancers

Targeted Depletion of Polo-Like Kinase (Plk) 1 Through Lentiviral shRNA or a Small-Molecule Inhibitor Causes Mitotic Catastrophe and Induction of Apoptosis in Human Melanoma Cells

Expression and possible role of hPTTG1/securin in cutaneous malignant melanoma

Antagonism of p66shc by melanoma inhibitory activity

RaLP, a new member of the Src homology and collagen family, regulates cell migration and tumor growth of metastatic melanomas

Melanoma: targeting signaling pathways and RaLP

Deregulated E2F transcriptional activity in autonomously growing melanoma cells

Arpc1b gene is a candidate prediction marker for choroidal malignant melanomas sensitive to radiotherapy

A multi-marker assay to distinguish malignant melanomas from benign nevi

BCCIP functions through p53 to regulate the expression of p21Waf1/Cip1

Identification of genes expressed in malignant cells that promote invasion

Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment

A potential immune escape mechanism by melanoma cells through the activation of chemokine-induced T cell death

Multiplex analysis of serum cytokines in melanoma patients treated with interferon-alpha2b

Regulation of centromeric cohesion by sororin independently of the APC/C

Sororin is required for stable binding of cohesin to chromatin and for sister chromatid cohesion in interphase

Sororin, the cell cycle and sister chromatid cohesion

Sororin, a substrate of the anaphase-promoting complex, is required for sister chromatid cohesion in vertebrates

Cooperative roles of c-Abl and Cdk5 in regulation of p53 in response to oxidative stress

Gene expression signatures for tumor progression, tumor subtype, and tumor thickness in laser-microdissected melanoma tissues

A receptor for the malarial parasite Plasmodium vivax: the erythrocyte chemokine receptor

MGSA/GRO transcription is differentially regulated in normal retinal pigment epithelial and melanoma cells

Nuclear factor-kappa B activation by the CXC chemokine melanoma growth-stimulatory activity/growth-regulated protein involves the MEKK1/p38 mitogen-activated protein kinase pathway

Endothelin-1 induces CXCL1 and CXCL8 secretion in human melanoma cells

Gene expression profiling reveals cross-talk between melanoma and fibroblasts: implications for host-tumor interactions in metastasis

Threedimensional culture of melanoma cells profoundly affects gene expression profile: a high density oligonucleotide array study

Evidence of involvement of CXC-chemokines in proliferation of cultivated human melanocytes

Role of CXCL1 in tumorigenesis of melanoma

Melanoma growth stimulatory activity in primary malignant melanoma: prognostic significance

A novel NF-kappa B-inducing kinase-MAPK signaling pathway up-regulates NF-kappa B activity in melanoma cells

Induction of melanoma in murine macrophage inflammatory protein 2 transgenic mice heterozygous for inhibitor of kinase/alternate reading frame

Constitutive IkappaB kinase activity correlates with nuclear factor-kappaB activation in human melanoma cells

MGSA/GROmediated melanocyte transformation involves induction of Ras expression

The tumorigenic and angiogenic effects of MGSA/GRO proteins in melanoma

Autocrine and paracrine roles for growth factors in melanoma

Localization of MGSA/GRO protein in cutaneous lesions

Melanoma growth stimulatory activity: isolation from human melanoma tumors and characterization of tissue distribution

Immunoaffinity purification of melanoma growth stimulatory activity

Characterization of the role of melanoma growth stimulatory activity (MGSA) in the growth of normal melanocytes, nevocytes, and malignant melanocytes

Enhanced tumor-forming capacity for immortalized melanocytes expressing melanoma growth stimulatory activity/growth-regulated cytokine beta and gamma proteins

Expression and migratory analysis of 5 human uveal melanoma cell lines for CXCL12, CXCL8, CXCL1, and HGF

Inhibition of multiple vascular endothelial growth factor receptors (VEGFR) blocks lymph node metastases but inhibition of VEGFR-2 is sufficient to sensitize tumor cells to platinum-based chemotherapeutics

Pilot study on the interaction between B16 melanoma cell-line and bone-marrow derived mesenchymal stem cells

Circulating serum levels of angiogenic factors and vascular endothelial growth factor receptors 1 and 2 in melanoma patients

Analysis of the tyrosine kinome in melanoma reveals recurrent mutations in ERBB4

The role of vascular endothelial growth factors and their receptors in malignant melanomas

The role of circulating angiogenic factors in patients operated on for localized malignant melanoma

Simultaneous blockade of VEGFR-1 and VEGFR-2 activation is necessary to efficiently inhibit experimental melanoma growth and metastasis formation

Flt-1 intraceptor induces the unfolded protein response, apoptotic factors, and regression of murine injury-induced corneal neovascularization

An autocrine loop directed by the vascular endothelial growth factor promotes invasiveness of human melanoma cells

Overproduction of VEGF concomitantly expressed with its receptors promotes growth and survival of melanoma cells through MAPK and PI3K signaling

MMP9 induction by vascular endothelial growth factor receptor-1 is involved in lungspecific metastasis

Release and complex formation of soluble VEGFR-1 from endothelial cells and biological fluids

Angiogenesis and vascular growth factor receptor expression in malignant melanoma

Association of increased levels of heavychain ferritin with increased CD4+ CD25+ regulatory T-cell levels in patients with melanoma

Heavy chain ferritin activates regulatory T cells by induction of changes in dendritic cells

Immunosuppressive effects of melanoma-derived heavy-chain ferritin are dependent on stimulation of IL-10 production

Ferritin heavy chain upregulation by NF-kappaB inhibits TNFalpha-induced apoptosis by suppressing reactive oxygen species

Sealing of chromosomal DNA nicks during nucleotide excision repair requires XRCC1 and DNA ligase III alpha in a cell-cycle-specific manner

Gene expression profiles in radiation workers occupationally exposed to ionizing radiation

Human melanoma cells secrete and respond to placenta growth factor and vascular endothelial growth factor

Expression patterns of placenta growth factor in human melanocytic cell lines

Neuropilins in tumor biology

Semaphorin signaling in cancer cells and in cells of the tumor microenvironment-two sides of a coin

From plasma membrane to cytoskeleton: a novel function for semaphorin 6A

Semaphorins at the interface of development and cancer

Comparative integromics on non-canonical WNT or planar cell polarity signaling molecules: transcriptional mechanism of PTK7 in colorectal cancer and that of SEMA6A in undifferentiated ES cells

Deciphering developmental stages of adult myelopoiesis

Downregulation of CCN3 expression as a potential mechanism for melanoma progression

Osteopontin expression in normal skin and non-melanoma skin tumors

Osteopontin stimulates melanoma growth and lung metastasis through NIK/MEKK1-dependent MMP-9 activation pathways

Osteonectin downregulates Ecadherin, induces osteopontin and focal adhesion kinase activity stimulating an invasive melanoma phenotype

Osteopontin expression in Spitz nevi

Serum markers to detect metastatic uveal melanoma

Osteopontin and the skin: multiple emerging roles in cutaneous biology and pathology

The Hedgehog Pathway Transcription Factor GLI1 Promotes Malignant Behavior of Cancer Cells by Up-regulating Osteopontin

Expression of phosphorylated-STAT3 and osteopontin and their correlation in melanoma

Matricellular proteins produced by melanocytes and melanomas: in search for functions

Osteopontin and 'melanoma inhibitory activity': comparison of two serological tumor markers in metastatic uveal melanoma patients

Extracellular and intracellular mechanisms that mediate the metastatic activity of exogenous osteopontin

Endothelin signaling axis activates osteopontin expression through PI3 kinase pathway in A375 melanoma cells

A highthroughput study in melanoma identifies epithelial-mesenchymal transition as a major determinant of metastasis

Serum osteopontin, an enhancer of tumor metastasis to bone, promotes B16 melanoma cell migration

Gene-specific fluorescence in-situ hybridization analysis on tissue microarray to refine the region of chromosome 20q amplification in melanoma

Identification of proteins regulated by interferon-alpha in resistant and sensitive malignant melanoma cell lines

A molecular correlate to the Gleason grading system for prostate adenocarcinoma

Ink4c and Pten constrain a positive regulatory loop between cell growth and cell cycle control

Survey of differentially methylated promoters in prostate cancer cell lines

Transfer of functional prostasomal CD59 of metastatic prostatic cancer cell origin protects cells against complement attack

Differentially expressed genes in two LNCaP prostate cancer cell lines reflecting changes during prostate cancer progression

Amine oxidase activities in chemically-induced mammary cancer in the rat

The significance of monoamine oxidase-A expression in high grade prostate cancer

From androgen receptor to the general transcription factor TFIIH. Identification of cdk activating kinase (CAK) as an androgen receptor NH(2)-terminal associated coactivator

Alphamethylacyl-CoA racemase-an 'obscure' metabolic enzyme takes centre stage

Biopsy tissue microarray study of Ki-67 expression in untreated, localized prostate cancer managed by active surveillance

Alpha-methylacyl-CoA racemase (P504S) expression in evolving carcinomas within benign prostatic hyperplasia and in cancers of the transition zone

Expression of alpha-methylacyl-CoA racemase (P504s) in various malignant neoplasms and normal tissues: astudy of 761 cases

Using an AMACR (P504S)/34betaE12/p63 cocktail for the detection of small focal prostate carcinoma in needle biopsy specimens

Diagnostic utility of alpha-methylacyl CoA racemase (P504S) on prostate needle biopsy

Discovery and clinical application of a novel prostate cancer marker: alpha-methylacyl CoA racemase (P504S)

Alphamethylacyl-CoA racemase: a multi-institutional study of a new prostate cancer marker

Quantitative immunohistochemical detection of the molecular expression patterns in proliferative inflammatory atrophy

The importance of determining the aggressiveness of prostate cancer using serum and tissue molecular markers

GOLPH2 protein expression as a novel tissue biomarker for prostate cancer: implications for tissue-based diagnostics

Optimization of laser capture microdissection and RNA amplification for gene expression profiling of prostate cancer

) alpha-Methylacyl-CoA racemase: expression levels of this novel cancer biomarker depend on tumor differentiation

Elevated alpha-methylacyl-CoA racemase enzymatic activity in prostate cancer

Comparison of monoclonal antibody (P504S) and polyclonal antibody to alpha methylacyl-CoA racemase (AMACR) in the work-up of prostate cancer

Immunohistochemical detection of carcinoma in radical prostatectomy specimens following hormone therapy

Basal cell subpopulation as putative human prostate carcinoma stem cells

A statistical method for identifying differential gene-gene co-expression patterns

Alphamethylacyl-CoA racemase (AMACR/P504S) protein expression in urothelial carcinoma of the upper urinary tract correlates with tumour progression

Sequence variation in alpha-methylacyl-CoA racemase and risk of early-onset and familial prostate cancer

Malignant transformation of human benign prostate epithelial cells by high linear energy transfer alpha-particles

Expression of alpha-methylacylcoenzyme A racemase in dysplastic Barrett's epithelium

Quantitative analysis of a panel of gene expression in prostate cancer-with emphasis on NPY expression analysis

The value of using an AMACR/34betaE12/p63 cocktail double staining for diagnosis of prostate carcinoma

Decreased gene expression of steroid 5 alpha-reductase 2 in human prostate cancer: implications for finasteride therapy of prostate carcinoma

Alpha-methylacyl-CoA racemase: a new molecular marker for prostate cancer

Alpha-methylacyl-CoA racemase: a variably sensitive immunohistochemical marker for the diagnosis of small prostate cancer foci on needle biopsy

Neoadjuvant docetaxel treatment for locally advanced prostate cancer: a clinicopathologic study

Biomarkers for Prostate Cancer

Method for quantification of a prostate cancer biomarker in urine without sample preparation

Effects of the dual 5 alpha-reductase inhibitor dutasteride on apoptosis in primary cultures of prostate cancer epithelial cells and cell lines

Routine immunohistochemical staining for high-molecular weight cytokeratin 34-beta and alpha-methylacyl CoA racemase (P504S) in postirradiation prostate biopsies

Search for residual prostate cancer on pT0 radical prostatectomy after positive biopsy

Branched fatty acids in dairy and beef products markedly enhance alpha-methylacyl-CoA racemase expression in prostate cancer cells in vitro

Alpha-methyl CoA racemase expression in renal cell carcinomas

Diagnostic utility of a p63/alpha-methyl-CoA-racemase (p504s) cocktail in atypical foci in the prostate

Value of new prostate cancer markers: alpha methylacyl CoA racemase (P504S) and p63

Evaluation of p63 and p504s markers for the diagnosis of prostate cancer

A preliminary study of the baboon prostate pathophysiology

Alternative spliced variants of the alpha-methylacyl-CoA racemase gene and their expression in prostate cancer

A variant of the alpha-methyl-acyl-CoA racemase gene created by a deletion in exon 5 and its expression in prostate cancer

Utility of alpha-methylacyl coenzyme A racemase (p504s antibody) as a diagnostic immunohistochemical marker for cancer

Alpha-methylacyl-CoA racemase as a marker in the differential diagnosis of metanephric adenoma

Frequent overexpression of ETS-related gene-1 (ERG1) in prostate cancer transcriptome

Human telomerase and alpha-methylacyl-coenzyme A racemase in prostatic carcinoma. A comparative immunohistochemical study

Prostate cancer detection on urinalysis for alpha methylacyl coenzyme a racemase protein

Decreased alpha-methylacyl CoA racemase expression in localized prostate cancer is associated with an increased rate of biochemical recurrence and cancer-specific death

Quantitative determination of expression of the prostate cancer protein alphamethylacyl-CoA racemase using automated quantitative analysis (AQUA): a novel paradigm for automated and continuous biomarker measurements

) alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer

Prostatic ductal adenocarcinoma presenting as a urethral polyp: a clinicopathological study of eight cases of a lesion with the potential to be misdiagnosed as a benign prostatic urethral polyp

) alpha-Methylacyl coenzyme A racemase, Ki-67, and topoisomerase IIalpha in cystoprostatectomies with incidental prostate cancer

Emerging biomarkers for the diagnosis and prognosis of prostate cancer

Extraction and processing of high quality RNA from impalpable and macroscopically invisible prostate cancer for microarray gene expression analysis

Kinetic fluorescence reverse transcriptase-polymerase chain reaction for alphamethylacyl CoA racemase distinguishes prostate cancer from benign lesions

Expression profiling identifies a novel alpha-methylacyl-CoA racemase exon with fumarate hydratase homology

Alphamethylacyl CoA racemase in pulmonary adenocarcinoma, squamous cell carcinoma, and neuroendocrine tumors: expression and survival analysis

Isolation of human prostatic epithelial plasma membranes for proteomics using mirror image tissue banking of radical prostatectomy specimens

Expression of alphamethylacyl-CoA racemase (P504S) in nephrogenic adenoma: a significant immunohistochemical pitfall compounding the differential diagnosis with prostatic adenocarcinoma

Oct4A is expressed by a subpopulation of prostate neuroendocrine cells

Humoral immune response to alpha-methylacyl-CoA racemase and prostate cancer

Comparison of annexin II, p63 and alpha-methylacyl-CoA racemase immunoreactivity in prostatic tissue: a tissue microarray study

Ancillary alpha-methylacyl-CoA racemase immunocytochemistry in the diagnosis of adenocarcinoma of the prostate in urinary cytology: a case report

Alpha-methylacyl-CoA racemase (P504S)/34betaE12/p63 triple cocktail stain in prostatic adenocarcinoma after hormonal therapy

Differences between latent and clinical prostate carcinomas: lower cell proliferation activity in latent cases

Variation of alpha-methylacyl-CoA racemase expression in prostate adenocarcinoma cases receiving hormonal therapy

Phytanic acid, AMACR and prostate cancer risk

Transcriptome analysis of human colon Caco-2 cells exposed to sulforaphane

Expression of alpha-methylacyl-CoA racemase in papillary renal cell carcinoma

Alpha-Methylacyl-CoA Racemase Expression is Upregulated in Gastric Adenocarcinoma: A Study of 249 Cases

Sensitivity of P504S/alpha-methylacyl-CoA racemase (AMACR) immunohistochemistry for the detection of prostate carcinoma on stored needle biopsies

Diagnostic utility of immunohistochemistry in morphologically difficult prostate cancer: review of current literature

The prostate-specific G-protein coupled receptors PSGR and PSGR2 are prostate cancer biomarkers that are complementary to alpha-methylacyl-CoA racemase

Partial atrophy on prostate needle biopsy cores: a morphologic and immunohistochemical study

Abundant expression of AMACR in many distinct tumour types

Peroxisomal disorders affecting phytanic acid alphaoxidation: a review

Alpha-methylacyl-CoA racemase protein expression is associated with the degree of differentiation in breast cancer using quantitative image analysis

Analysis of alpha-methylacyl-CoA racemase (P504S) expression in high-grade prostatic intraepithelial neoplasia

Serum levels of phytanic acid are associated with prostate cancer risk

Detection of alpha-methylacyl-coenzyme A racemase in postradiation prostatic adenocarcinoma

Tumor suppressor activity of glucocorticoid receptor in the prostate

Detection of alpha-methylacyl-coenzyme-A racemase transcripts in blood and urine samples of prostate cancer patients

Alphamethylacyl-CoA racemase as an androgen-independent growth modifier in prostate cancer

Peroxisomal branched chain fatty acid beta-oxidation pathway is upregulated in prostate cancer

A nonclassic CCAAT enhancer element binding protein binding site contributes to alpha-methylacyl-CoA racemase expression in prostate cancer

Sequence variants of alpha-methylacyl-CoA racemase are associated with prostate cancer risk

How often does alphamethylacyl-CoA-racemase contribute to resolving an atypical diagnosis on prostate needle biopsy beyond that provided by basal cell markers?

Alpha-Methylacyl-CoA racemase: a novel tumor marker over-expressed in several human cancers and their precursor lesions

Expression and diagnostic utility of alphamethylacyl-CoA-racemase (P504S) in foamy gland and pseudohyperplastic prostate cancer

A novel diagnostic test for prostate cancer emerges from the determination of alphamethylacyl-coenzyme a racemase in prostatic secretions

Deletion hotspots in AMACR promoter CpG island are cis-regulatory elements controlling the gene expression in the colon

Gene expression profiles in the PC-3 human prostate cancer cells induced by NKX3.1

Usefulness of cytokeratin 5/6 and AMACR applied as double sequential immunostains for diagnostic assessment of problematic prostate specimens

Conversion of prostate cancer from hormone independency to dependency due to AMACR inhibition: involvement of increased AR expression and decreased IGF1 expression

Oct4A is expressed by a subpopulation of prostate neuroendocrine cells

A duplex quantitative polymerase chain reaction assay based on quantification of alphamethylacyl-CoA racemase transcripts and prostate cancer antigen 3 in urine sediments improved diagnostic accuracy for prostate cancer

A continuous assay for alpha-methylacylcoenzyme A racemase using circular dichroism

Group IIA phospholipase A as a prognostic marker in prostate cancer: relevance to clinicopathological variables and disease-specific mortality

Biomarkers for prostate cancer

Immunohistochemical algorithms in prostate diagnostics: What's new?]. Pathologe

Alpha-methylacyl-CoA racemase (AMACR) in fine-needle aspiration specimens of prostate lesions

Biopsy tissue microarray study of Ki-67 expression in untreated, localized prostate cancer managed by active surveillance

Urine markers in monitoring for prostate cancer

Expression of alpha-methylacyl-CoA racemase (P504S) in sebaceous neoplasms

Expression of prostatic acid phosphatase (PSAP) in transurethral resection specimens of the prostate is predictive of histopathologic tumor stage in subsequent radical prostatectomies

Prostate carcinogenesis induced by N-methyl-N-nitrosourea (mnu) in gerbils: Histopathological diagnosis and potential invasiveness mediated by extracellular matrix components

Synthesis and use of isotope-labelled substrates for a mechanistic study on human alpha-methylacyl-CoA racemase 1A (AMACR; P504S)

S-100A1 Is a Reliable Marker in Distinguishing Nephrogenic Adenoma From Prostatic Adenocarcinoma

Molecular cloning and preliminary analysis of the human alpha-methylacyl-CoA racemase promoter

Autopsy evaluation of a prostate cancer case treated with brachytherapy

Loss of heterozygosity (LOH), malignancy grade and clonality in microdissected prostate cancer

Allelic loss and microsatellite instability in prostate cancers in Japan

tumour suppressor gene mutations in benign prostatic hyperplasia and prostate cancer

Mutations in BRCA1 and BRCA2 and predisposition to prostate cancer

BRCA1a has antitumor activity in TN breast, ovarian and prostate cancers

Coexpression of the mutated BRCA1 mRNA and p53 mRNA and its association in Chinese prostate cancer

Exploiting the Achilles heel of cancer: the therapeutic potential of poly(ADP-ribose) polymerase inhibitors in BRCA2-defective cancer

Prostate cancer patients with BRCA2 mutation face poor survival

BRCA1 mutations and prostate cancer in Poland

Common variation in the BRCA1 gene and prostate cancer risk

BRCA1 in special populations

Male BRCA1 and BRCA2 mutation carriers: a pilot study investigating medical characteristics of patients participating in a prostate cancer prevention clinic

Truncating BRCA1 mutations are uncommon in a cohort of hereditary prostate cancer families with evidence of linkage to 17q markers

Unravelling the genetics of prostate cancer

Prostate-cancer screening targets men with BRCA mutations

BRCA1 and prostate cancer

gene mutational rate, Gleason score, and BK virus infection in prostate adenocarcinoma: Is there a correlation

TP53 mutation in prostate needle biopsies-comparison with patients follow-up

Shared TP53 gene mutation in morphologically and phenotypically distinct concurrent primary small cell neuroendocrine carcinoma and adenocarcinoma of the prostate

Unique substitution of CHEK2 and TP53 mutations implicated in primary prostate tumors and cancer cell lines

Molecular biology in prostate cancer

A microdissection approach to detect molecular markers during progression of prostate cancer

Molecular biology of prostate cancer progression

Molecular heterogeneity in prostate cancer: can TP53 mutation unravel tumorigenesis

abnormalities in primary prostate cancer: single-strand conformation polymorphism analysis of complementary DNA in comparison with genomic DNA. The Cooperative Prostate Network

status and prognosis of locally advanced prostatic adenocarcinoma: a study based on RTOG 8610

Localization of prostate cancer metastasis-suppressor activity on human chromosome 17

Correlation of genetic and immunodetection of TP53 mutations in malignant and benign prostate tissues

Molecular markers for predicting prostate cancer stage and survival

Role in tumorigenesis of silent mutations in the TP53 gene

Chromosomal imbalances, loss of heterozygosity, and immunohistochemical expression of TP53, RB1, and PTEN in intraductal cancer, intraepithelial neoplasia, and invasive adenocarcinoma of the prostate

Increase of androgen-induced cell death and androgen receptor transactivation by BRCA1 in prostate cancer cells

Androgen regulation of the androgen receptor coregulators

Breast cancer susceptibility gene 1 (BRCAI) is a coactivator of the androgen receptor

Common mutations in BRCA1 and BRCA2 do not contribute to early prostate cancer in Jewish men

BRCA1 in hormonal carcinogenesis: basic and clinical research

Role of direct interaction in BRCA1 inhibition of estrogen receptor activity

BRCA1 regulates gene expression for orderly mitotic progression

The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis

Constitutive activation of JAK-STAT3 signaling by BRCA1 in human prostate cancer cells

Methylation analysis of BRCA1, RASSF1, GSTP1 and EPHB2 promoters in prostate biopsies according to different degrees of malignancy

PSF and p54(nrb)/NonO-multi-functional nuclear proteins

The PSF.p54nrb complex is a novel Mnk substrate that binds the mRNA for tumor necrosis factor alpha

PTB-associated splicing factor regulates growth factor-stimulated gene expression in mammalian cells

Transcriptional activity of androgen receptor is modulated by two RNA splicing factors, PSF and p54nrb

PSPC1, NONO, and SFPQ are expressed in mouse Sertoli cells and may function as coregulators of androgen receptor-mediated transcription

acts as a transcriptional coactivator for activation function 1 of the human androgen receptor

Regulation of RNA-polymerase-II-dependent transcription by N-WASP and its nuclearbinding partners

Identification and characterization of the protein-associated splicing factor as a negative coregulator of the progesterone receptor

Inhibition of dendritic cell generation and function by serum from prostate cancer patients: correlation with serum-free PSA

Impact of the tumor microenvironment on host infiltrating cells and the efficacy of flt3-ligand combination immunotherapy evaluated in a treatment model of mouse prostate cancer

Prostate tumor microenvironment alters immune cells and prevents long-term survival in an orthotopic mouse model following flt3-ligand/CD40-ligand immunotherapy

The influence of alpha1-antagonist on the expression pattern of TNF receptor family in primary culture of prostate epithelial cells from BPH patients

Adenovirus-mediated CD40 ligand therapy induces tumor cell apoptosis and systemic immunity in the TRAMP-C2 mouse prostate cancer model

Avoiding Tolerance Against Prostatic Antigens With Subdominant Peptide Epitopes

Avoiding tolerance against prostatic antigens with subdominant peptide epitopes

Plasma tissue factor antigen in localized prostate cancer: distribution, clinical significance and correlation with haemostatic activation markers

Enhanced activation of human dendritic cells by inducible CD40 and Toll-like receptor-4 ligation

Mature dendritic cells induce tumor-specific type 1 regulatory T cells

CD40 is not detected on human prostate cancer cells by immunohistologic techniques

Flt3 ligand expands dendritic cell numbers in normal and malignant murine prostate

Functional dichotomy in CD40 reciprocally regulates effector T cell functions

Dendritic cell gene therapy

CD40 expression in prostate cancer: a potential diagnostic and therapeutic molecule

A population of HLA-DR+ immature cells accumulates in the blood dendritic cell compartment of patients with different types of cancer

Cytokine-mediated protection of human dendritic cells from prostate cancerinduced apoptosis is regulated by the Bcl-2 family of proteins

Fas-mediated apoptosis in human prostatic carcinoma cell lines

Increased function and survival of IL-15-transduced human dendritic cells are mediated by up-regulation of IL-15Ralpha and Bcl-2

Expression and function of the complement membrane attack complex inhibitor protectin (CD59) in human prostate cancer

Increased CD59 protein expression predicts a PSA relapse in patients after radical prostatectomy

Differential expression of complement regulatory proteins decay-accelerating factor (CD55), membrane cofactor protein (CD46) and CD59 during human spermatogenesis

Flow cytometric technique for determination of prostasomal quantity, size and expression of CD10, CD13, CD26 and CD59 in human seminal plasma

Transfer of prostasomal CD59 to CD59-deficient red blood cells results in protection against complement-mediated hemolysis

Identification of extracellular delta-catenin accumulation for prostate cancer detection

Possible immunoprotective and angiogenesis-promoting roles for malignant cell-derived prostasomes: a new paradigm for prostatic cancer?

Complement resistance of human carcinoma cells depends on membrane regulatory proteins, protein kinases and sialic acid

The nuclear lamina. Both a structural framework and a platform for genome organization

The nuclear envelope, a key structure in cellular integrity and gene expression

Mouse models of the laminopathies

Laminopathies'': a wide spectrum of human diseases

Phenomics and lamins: from disease to therapy

Adult stem cell maintenance and tissue regeneration in the ageing context: the role for A-type lamins as intrinsic modulators of ageing in adult stem cells and their niches

Nuclear lamin A/C deficiency induces defects in cell mechanics, polarization, and migration

Lamin A/C deficiency causes defective nuclear mechanics and mechanotransduction

Decreased mechanical stiffness in LMNA2/2 cells is caused by defective nucleo-cytoskeletal integrity: implications for the development of laminopathies

Stabilization of the retinoblastoma protein by A-type nuclear lamins is required for INK4A-mediated cell cycle arrest

A-type lamins regulate retinoblastoma protein function by promoting subnuclear localization and preventing proteasomal degradation

Nuclear lamin A inhibits adipocyte differentiation: implications for Dunnigan-type familial partial lipodystrophy

A-type lamins: guardians of the soma?

Downregulation of human X-box binding protein 1 (hXBP-1) expression correlates with tumor progression in human prostate cancers

The proapoptotic kinase Mst1 and its caspase cleavage products are direct inhibitors of Akt1

Gene network and canonical pathway analysis in prostate cancer: a microarray study

Biomarker expression patterns that correlate with high grade features in treatment naive, organ-confined prostate cancer

NF-kappaB2 processing and p52 nuclear accumulation after androgenic stimulation of LNCaP prostate cancer cells

Diverse effects of zinc on NF-kappaB and AP-1 transcription factors: implications for prostate cancer progression

Nuclear factor-kappaB nuclear localization is predictive of biochemical recurrence in patients with positive margin prostate cancer

Id-1 expression promotes cell survival through activation of NF-kappaB signalling pathway in prostate cancer cells

Suppression of hormone-refractory prostate cancer by a novel nuclear factor kappaB inhibitor in nude mice

Mechanisms of constitutive NF-kappaB activation in human prostate cancer cells

Bcl-2 suppresses apoptosis resulting from disruption of the NF-kappa B survival pathway

Gene expression profiling of human prostate cancer stem cells reveals a proinflammatory phenotype and the importance of extracellular matrix interactions

Molecular imaging of NF-kappaB in prostate tissue after systemic administration of IL-1 beta

TNF/IL-1/NIK/NF-kappa B transduction pathway: a comparative study in normal and pathological human prostate (benign hyperplasia and carcinoma)

Proteasome inhibitors induce apoptosis of prostate cancer cells by inducing nuclear translocation of IkappaBalpha

Targeting the receptor activator of nuclear factor-kappaB (RANK) ligand in prostate cancer bone metastases

Pomegranate extract inhibits androgen-independent prostate cancer growth through a nuclear factor-kappaB-dependent mechanism

The nuclear factor-kappaB pathway controls the progression of prostate cancer to androgenindependent growth

A new prostate cancer therapeutic approach: combination of androgen ablation with COX-2 inhibitor

Inhibitory effect of snake venom toxin from Vipera lebetina turanica on hormone-refractory human prostate cancer cell growth: induction of apoptosis through inactivation of nuclear factor kappaB

Prostate cancer chemoprevention by silibinin: bench to bedside

Genistein inhibits radiation-induced activation of NF-kappaB in prostate cancer cells promoting apoptosis and G2/M cell cycle arrest

NF-kappaB inhibition increases chemosensitivity to trichostatin A-induced cell death of Ki-Ras-transformed human prostate epithelial cells

Involvement of the TNF-alpha autocrine-paracrine loop, via NF-kappaB and YY1, in the regulation of tumor cell resistance to Fas-induced apoptosis

Estrogens and antiestrogens as etiological factors and therapeutics for prostate cancer

NF-kappaB activation upregulates fibroblast growth factor 8 expression in prostate cancer cells

Anticancer potential of silymarin: from bench to bed side

Blockage of NF-kappaB induces serine 15 phosphorylation of mutant p53 by JNK kinase in prostate cancer cells

Skp2 enhances polyubiquitination and degradation of TIS21/BTG2/PC3, tumor suppressor protein, at the downstream of FoxM1

Triiodothyronine modulates cell proliferation of human prostatic carcinoma cells by downregulation of the B-cell translocation gene 2

B cell translocation gene 2 enhances susceptibility of HeLa cells to doxorubicin-induced oxidative damage

TIS21 (/BTG2/PC3) as a link between ageing and cancer: cell cycle regulator and endogenous cell death molecule

Expression of B-cell translocation gene 2 protein in normal human tissues

Antiproliferative B cell translocation gene 2 protein is down-regulated posttranscriptionally as an early event in prostate carcinogenesis

Identification of genes associated with stromal hyperplasia and glandular atrophy of the prostate by mRNA differential display

Role of connective tissue growth factor in fibronectin synthesis in cultured human prostate stromal cells

Stromal expression of connective tissue growth factor promotes angiogenesis and prostate cancer tumorigenesis

Analysis of gene expression during staurosporine-induced neuronal differentiation of human prostate cancer cells

Profiling molecular targets of TGF-beta1 in prostate fibroblast-to-myofibroblast transdifferentiation

Expression of androgen receptor coregulatory proteins in prostate cancer and stromal-cell culture models

FHL2, a novel tissue-specific coactivator of the androgen receptor

Four and a Half LIM Domain 2 alters the impact of Aryl Hydrocarbon Receptor on Androgen Receptor transcriptional activity

Differently regulated androgen receptor transcriptional complex in prostate cancer compared with normal prostate

Suppression of FOXO1 activity by FHL2 through SIRT1-mediated deacetylation

The transcriptional coactivator FHL2 transmits Rho signals from the cell membrane into the nucleus

Functional epigenomics identifies genes frequently silenced in prostate cancer

Expression level and DNA methylation status of glutathione-S-transferase genes in normal murine prostate and TRAMP tumors

Function of JunB in transient amplifying cell senescence and progression of human prostate cancer

KAI1 promoter activity is dependent on p53, junB and AP2: evidence for a possible mechanism underlying loss of KAI1 expression in cancer cells

Inhibition of prostate tumor growth by overexpression of NudC, a microtubule motorassociated protein

Control of androgen receptor signaling in prostate cancer by the cochaperone small glutamine rich tetratricopeptide repeat containing protein alpha

Signal transducer and activator of transcription-6 (STAT6) is a constitutively expressed survival factor in human prostate cancer

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data

Molecular features of the transition from prostatic intraepithelial neoplasia (PIN) to prostate cancer: genome-wide gene-expression profiles of prostate cancers and PINs

Analysis of integrin alpha7 mutations in prostate cancer, liver cancer, glioblastoma multiforme, and leiomyosarcoma

The biology of cancer

The chromosomal basis of cancer

Gene panel model predictive of outcome in men at high-risk of systemic progression and death from prostate cancer after radical retropubic prostatectomy

Aneuploidy and rapid cell proliferation in recurrent prostate cancers with androgen receptor gene amplification

Prognostic value of DNA analysis of prostate adenocarcinoma: correlation to clinicopathologic predictors

Heterogeneity in prostate cancer: prostate specific antigen (PSA) and DNA cytophotometry

Identification of patients with low-risk for aneuploidy: comparative discriminatory models using linear and machinelearning classifiers in prostate cancer

Prognostic factors in prostate cancer

Stage B Prostate Cancer: Correlation of DNA Ploidy Analysis With Histological and Clinical Parameters

ETS gene fusions in prostate cancer

Unusually stable abnormal karyotype in a highly aggressive melanoma negative for telomerase activity

Repetitive DNA alterations in human skin cancers

Emerging insights into the molecular pathogenesis of uveal melanoma

Integrative genomic analysis of aneuploidy in uveal melanoma

Decreased DNA ploidy may constitute a mechanism of the reduced malignant behavior of B16 melanoma in aged mice

High frequency of tetraploidy detected in malignant melanoma of Japanese patients by fluorescence in situ hybridization

Analysis of the DNA content in the progression of recurrent and metastatic melanomas

Evaluation of DNA ploidy and degree of DNA abnormality in benign and malignant melanocytic lesions of the skin using video imaging

Association of genomic imbalances with resistance to therapeutic drugs in human melanoma cell lines

Highgrade prostate intraepithelial neoplasia shares cytogenetic alterations with invasive prostate cancer

Persistent exposure to Mycoplasma induces malignant transformation of human prostate cells

Profiling cytogenetic diversity with entropy-based karyotypic analysis

A Farewell to Entropy: Statistical Thermodynamics based on Information World

Can Entropy and ''order'' increase together ?

Entropy measures quantify global splicing disorders in cancer

Computing the optimal protocol for finite-time processes in stochastic thermodynamics

Optimal finite-time endoreversible processes

Optimal process paths for endoreversible systems

Altered mode of allelic replication accompanied by aneuploidy in peripheral blood lymphocytes of prostate cancer patients

A thermodynamic interpretation of malignancy: do the genes come later?

The storage of energy as a cause of malignant transformation: a 7-phase model of carcinogenesis

Biology of cancer: thermodynamic answers to some questions

Cervical cancer as a natural phenomenon

Information of genome sequences and molecular basis of cancer

Maxwell's demon and Smoluchowski's trap door

Maxwell's demon assisted thermodynamic cycle in superconducting quantum circuits

Ponderomotive ratchet in a uniform magnetic field

Protein folding and evolution are driven by the Maxwell Demon activity of proteins

Current drive in a ponderomotive potential with sign reversal

Global and local) fluctuations of phase space contraction in deterministic stationary nonequilibrium

The Szilard engine revisited: Entropy, macroscopic randomness, and symmetry breaking phase transitions

Hydrodynamic Maxwell demon in granular systems

Evolution of biological complexity

Adaptation and information in ontogenesis and phylogenesis. Increase of complexity and efficiency

Use of glucose 6-phosphate and hexokinase as an ATP regenerating system by the Ca(2+)-ATPase of sarcoplasmic reticulum and submitochondrial particles

Maxwell's Demon Anderson PW

Comment on ''Quantitative limits on the ability of a Maxwell demon to extract work from heat

Quantitative limits on the ability of a Maxwell demon to extract work from heat

Folding and misfolding mechanisms of the p53 DNA binding domain at physiological temperature

Kinetic partitioning during folding of the p53 DNA binding domain

p53: guardian of the genome and policeman of the oncogenes

in mitochondria enhances the accuracy of DNA synthesis

Structural biology of the p53 tumour suppressor

Temperature sensitivity for conformation is an intrinsic property of wild-type p53

Adaptive response of the skin to UVB damage: role of the p53 protein

Tumorigenic conversion of immortal human skin keratinocytes (HaCaT) by elevated temperature

The story of the discovery of aquaporins: convergent evolution of ideas-but who got there first?

Aquaporin water channels: molecular mechanisms for human diseases

The aquaporin family of water channels in kidney

Aquaporin CHIP: the archetypal molecular water channel

ABH and Colton blood group antigens on aquaporin-1, the human red cell water channel protein

Water channel proteins: from their discovery in 1985 in Cluj-Napoca, Romania, to the 2003 Nobel Prize in Chemistry

Structure-function relationships in aquaporins

A brief survey of aquaporins and their implications for renal physiology

The structural basis of water permeation and proton exclusion in aquaporins

Concerted action of two cation filters in the aquaporin water channel

Knock-out models reveal new aquaporin functions

Involvement of aquaporin-5 in differentiation of human gastric cancer cells

Aquaporins-new players in cancer biology

Role of human aquaporin 5 in colorectal carcinogenesis

Prevention of skin tumorigenesis and impairment of epidermal cell proliferation by targeted aquaporin-3 gene disruption

Aquaporin-3 facilitates epidermal cell migration and proliferation during wound healing

Roles of aquaporin-3 in the epidermis

Expression profile of early lung adenocarcinoma: identification of MRP3 as a molecular marker for early progression

Expression of aquaporin 3 in the human prostate

Expression of aquaporin 3 (AQP3) in normal and neoplastic lung tissues

Role of aquaporin-7 and aquaporin-9 in glycerol metabolism

Genetic conservation of the GIL blood group determining aquaporin 3 gene in African and Caucasian populations

Aquaporin 9 is the major pathway for glycerol uptake by mouse erythrocytes, with implications for malarial virulence

pH regulated anion permeability of aquaporin-6

Temporal changes in expression of aquaporin3, -4, -5 and -8 in rat brains after permanent focal cerebral ischemia

Expression of renal aquaporins is down-regulated in children with congenital hydronephrosis

Aquaporins as potential drug targets for Meniere's disease and its related diseases

In situ fluorescence measurement of tear film [Na+], [K+], [Cl2], and pH in mice shows marked hypertonicity in aquaporin-5 deficiency

Regulation of aquaporin-2 trafficking

The role of water channel aquaporin 3 in the mechanism of TNF-alphamediated proinflammatory events: Implication in periodontal inflammation

Nephrogenic diabetes insipidus in mice lacking aquaporin-3 water channels

The role of aquaporins in dendritic cell macropinocytosis

Structure and function of aquaporin water channels

Selection of Discriminative Genes in Microarray Experiments using Mathematical Programming

Inhibition of the aquaporin 3 water channel increases the sensitivity of prostate cancer cells to cryotherapy

Point mutations in the aromatic/arginine region in aquaporin 1 allow passage of urea, glycerol, ammonia, and protons

Mechanism of aquaporin-4's fast and highly selective water conduction and proton exclusion

Dynamics and energetics of permeation through aquaporins. What do we learn from molecular dynamics simulations?

In vitro analysis and modification of aquaporin pore selectivity

Structure-function analysis of plant aquaporin AtPIP2;1 gating by divalent cations and protons

Ion exclusion mechanism in aquaporin at an atomistic level

Aquaporins with selectivity for unconventional permeants

Molecular mechanisms of conduction and selectivity in aquaporin water channels

Fast and selective ammonia transport by aquaporin-8

The high diversity and regulation of plant water channels

Charge delocalization in proton channels, I: the aquaporin channels and proton blockage

Regulation of selectivity and translocation of aquaporins: an update

Toward theoretical analysis of long-range proton transfer kinetics in biomolecular pumps

The barrier for proton transport in aquaporins as a challenge for electrostatic models: the role of protein relaxation in mutational calculations

The mechanism of proton exclusion in the aquaporin-1 water channel

What really prevents proton transport through aquaporin? Charge self-energy versus proton wire proposals

Control of the selectivity of the aquaporin water channel family by global orientational tuning

The aquaporins

Aquaporin-1 and HCO3(-)-Cl-transportermediated transport of CO2 across the human erythrocyte membrane

Ammonia and urea permeability of mammalian aquaporins

Expression and function of aquaporins in human skin: Is aquaporin-3 just a glycerol transporter

Physiological roles of glyceroltransporting aquaporins: the aquaglyceroporins

A role for mitochondrial aquaporins in cellular life-and-death decisions?

Functional expression of AQP3 in human skin epidermis and reconstructed epidermis

Skin hydration: a review on its molecular mechanisms

Hydrating skin by stimulating biosynthesis of aquaporins

Pores in the epidermis: aquaporins and tight junctions

Biophysical properties of epithelial water channels

BRCA1 localization to the telomere and its loss from the telomere in response to DNA damage

Disruption of BRCA1 function results in telomere lengthening and increased anaphase bridge formation in immortalized cell lines

The role of DNA damage response proteins at telomeresan ''integrative'' model

Correlating breakage-fusion-bridge events with the overall chromosomal instability and in vitro karyotype evolution in prostate cancer

Telomere shortening is an early somatic DNA alteration in human prostate tumorigenesis

Role of telomere dysfunction in aging and its detection by biomarkers

Telomere dysfunction and tumour suppression: the senescence connection

Dual roles of telomere dysfunction in initiation and suppression of tumorigenesis

Telomere dysfunction, genome instability and cancer

BRCA1 knock-down causes telomere dysfunction in mammary epithelial cells

Telomere dysfunction in aging and cancer

Telomere dysfunction increases mutation rate and genomic instability

Telomere dysfunction triggers extensive DNA fragmentation and evolution of complex chromosome abnormalities in human malignant tumors

DNA damage signalling prevents deleterious telomere addition at DNA breaks

The human Rap1 protein complex and modulation of telomere length

Small regulatory RNAs in mammals

Implications of micro-RNA profiling for cancer diagnosis

miRNAs: Little known mediators of oncogenesis

Regulation of epidermal growth factor receptor signaling in human cancer cells by microRNA-7

MicroRNAs and their potential for translation in prostate cancer

MicroRNA expression profiling in prostate cancer

Clusters of internally primed transcripts reveal novel long noncoding RNAs

Long, abundantly expressed non-coding transcripts are altered in cancer

Long non-coding RNAs: insights into functions

RNA regulation of epigenetic processes

The genetic signatures of noncoding RNAs

39 end processing of a long nuclearretained noncoding RNA yields a tRNA-like cytoplasmic RNA

DNA sequence of the translocation breakpoints in undifferentiated embryonal sarcoma arising in mesenchymal hamartoma of the liver harboring the t(11;19)(q11;q13.4) translocation

Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas

A transcriptional sketch of a primary human breast cancer by 454 deep sequencing

Metastasis associated lung adenocarcinoma transcript 1 (MALAT-1) is up-regulated in placenta previa increta/percreta and strongly associated with trophoblast-like cell invasion in vitro

Elucidating the molecular physiopathology of acute respiratory distress syndrome in severe acute respiratory syndrome patients

Expression profile of microRNAs in c-Myc induced mouse mammary tumors

Prognostic significance of drug-regulated genes in high-grade osteosarcoma

A screen for nuclear transcripts identifies two linked noncoding RNAs associated with SC35 splicing domains

A large noncoding RNA is a marker for murine hepatocellular carcinomas and a spectrum of human carcinomas

Phenotypic characterization of endometrial stromal sarcoma of the uterus

Genome-wide screening for prognosis-predicting genes in early-stage non-small-cell lung cancer

MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer

The interaction profile of homologous recombination repair proteins RAD51C, RAD51D and XRCC2 as determined by proteomic analysis

Paraspeckles: nuclear bodies built on long noncoding RNA

hnRNP M interacts with PSF and p54(nrb) and co-localizes within defined nuclear structures

Altered nuclear retention of mRNAs containing inverted repeats in human embryonic stem cells: functional role of a nuclear noncoding RNA

How to build a paraspeckle

An architectural role for a nuclear noncoding RNA: NEAT1 RNA is essential for the structure of paraspeckles

MENepsilon/beta noncoding RNAs are essential for structural integrity of nuclear paraspeckles

MEN epsilon/beta nuclear-retained non-coding RNAs are up-regulated upon muscle differentiation and are essential components of paraspeckles

nuclear domains: characterization and dynamics as a function of transcriptional activity

Subnuclear localization and dynamics of the Pre-mRNA 39 end processing factor mammalian cleavage factor I 68-kDa subunit

Functional studies of BCL11A: characterization of the conserved BCL11A-XL splice variant and its interaction with BCL6 in nuclear paraspeckles of germinal center B cells

Nucleocytoplasmic transport of fluorescent mRNA in living mammalian cells: nuclear mRNA export is coupled to ongoing gene transcription

Splicing speckles are not reservoirs of RNA polymerase II, but contain an inactive form, phosphorylated on serine2 residues of the C-terminal domain

Regulating gene expression through RNA nuclear retention

P54nrb forms a heterodimer with PSP1 that localizes to paraspeckles in an RNA-dependent manner

Dynamics of the mammalian nucleus: can microscopic movements help us to understand our genes?

Distinct sequence motifs within the 68-kDa subunit of cleavage factor Im mediate RNA binding, protein-protein interactions, and subcellular localization

Nucleolomics: an inventory of the nucleolus

Paraspeckles: a novel nuclear domain

A NEAT way of regulating nuclear export of mRNAs

Genomic structure, chromosomal localization and expression profile of a porcine long non-coding RNA isolated from long SAGE libraries

Apoptosis, cell cycle progression and gene expression in TP53-depleted HCT116 colon cancer cells in response to short-term 5-fluorouracil treatment

OCIA domain containing 2 is highly expressed in adenocarcinoma mixed subtype with bronchioloalveolar carcinoma component and is associated with better prognosis

Interacting endogenous and exogenous RNAi pathways in Caenorhabditis elegans

Inhibition of alloresponse by a human trophoblast non-coding RNA suppressing class II transactivator promoter III and major histocompatibility class II expression in murine Blymphocytes

Class II transactivator promoter activity is suppressed through regulation by a trophoblast noncoding RNA

MicroRNAs and other tiny endogenous RNAs in C. elegans

Human trophoblast noncoding RNA suppresses CIITA promoter III activity in murine B-lymphocytes

Role of human noncoding RNAs in the control of tumorigenesis

Intensive entropic non-triviality measure

Statistical complexity and disequilibrium

Distinguishing noise from chaos

Microarrays-identifying molecular portraits for prostate tumors with different Gleason patterns

Molecular portraits of human breast tumours

Molecular characterisation of soft tissue tumours: a gene expression study

Correlation Theory of Statistically Optimal Systems Gelbaum BR

Information Theory, Inference, and Learning Algorithms

A statistical measure of complexity

Analysis of symbolic sequences using the Jensen-Shannon divergence

Effect of chain length on fragility and thermodynamic scaling of the local segmental dynamics in poly(methylmethacrylate)

Length of time's arrow

Measuring thermodynamic length

Far-from-equilibrium measurements of thermodynamic length

Empirical Relationships between Species Richness, Evenness, and Proportional Diversity

The functional 2443T/C osteopontin promoter polymorphism influences osteopontin gene expression in melanoma cells via binding of c-Myb transcription factor

Tumor necrosis is associated with increased alphavbeta3 integrin expression and poor prognosis in nodular cutaneous melanomas

Osteopontin as a serologic marker for metastatic uveal melanoma: results of a pilot study

Stable overexpression of Smad7 in human melanoma cells impairs bone metastasis

Analysis of gene expression in the tumor-associated macrophage

Nuclear factor inducing kinase: a key regulator in osteopontin-induced MAPK/IkappaB kinase dependent NF-kappaB-mediated promatrix metalloproteinase-9 activation

Prostate-specific antigen modulates genes involved in bone remodeling and induces osteoblast differentiation of human osteosarcoma cell line SaOS-2

The authors would like to thank three Research Associates of our Centre (Osvaldo Rosso, Carlos Riveros and John Marsden) for discussions on this topic. We thank the first two, in particular to Osvaldo, for their collaboration on data cleaning and the computation of the Entropy and Jensen-Shannon divergences. We thank Marsden for his advice on editing the final draft. PM would also like to thank two stimulating discussions (decades apart) with Dr. Carlos Reigosa and Prof. Elizabeth Blackburn, as well as the invisible hand of Prof. Yaser Abu-Mostafa.