Differentially expressed androgen-regulated genes in androgen-sensitive tissues reveal potential biomarkers of early prostate cancer

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Potassium in Solid Cancers

Electrolyte disorders are a frequent finding in cancer patients. In the majority of cases the etiologies of such disorders are common to all cancer types (i.e. diuretic-induced hyponatremia or hypokalemia). Sometimes, electrolyte disorders are caused by paraneoplastic syndromes or are due to cancer therapy. Potassium is one of the most important electrolytes of the human body since it is involved in the regulation of muscle contraction, maintenance of the integrity of the skeleton, blood pressure and nerve transmission as well as in the normal function of cells. Potassium homeostasis is strictly regulated since the gap between the recommended daily dietary intake (120 mEq/day) and the levels stored in the extracellular fluid (around 70 mEq) is huge. Alterations of potassium homeostasis are frequent in cancer patients as well alterations in potassium channels, the transmembrane proteins that mediate potassium fluxes within the cells. The present chapter is focused on the clinical significance of potassium homeostasis and potassium channels in patients with solid tumors.

Immunomodulatory Role of Surfactant Protein-D in a Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) Model

Surfactant protein D (SP-D), a pattern recognition molecule, is emerging as a potent anti-tumoural innate immune defense molecule in a range of cancers. Previously, SP-D expression was found to be significantly downregulated at the malignant sites of human prostate adenocarcinoma and associated with an increasing Gleason score and severity. We recently reported selective induction of intrinsic apoptosis by a recombinant fragment of human SP-D (rfhSP-D) in the human Prostate cancer (PCa) biopsy explants and cells with glucose regulated protein of 78 (GRP78) as one of the key interacting partners. The present study evaluated the expression of SP-D in early and advanced stages of PCa using transgenic adenocarcinoma of mouse prostate (TRAMP) model. Both early and late stages of PCa showed significantly decreased SP-D mRNA expression and increased proteolytic degradation of SP-D protein. Systemic and tumoural immunophenotyping of TRAMP model revealed increased serine proteases producing granulocytes and polymorphonuclear myeloid-derived suppressor cells (PMN MDSCs) in the late stage; the serine proteases secreted by these cells could be involved in the degradation of SP-D. Susceptibility of rfhSP-D to elastase-mediated proteolysis provided the rationale to use an elastase-inhibitor to sustain intact rfhSP-D in the tumour microenvironment. The study revealed an immunomodulatory potential of rfhSP-D and elastase inhibitor, sivelestat, to induce macrophage polarization towards M1 with downregulation of PMN MDSCs in ex-vivo cultured TRAMP tumours. Furthermore, rfhSP-D induced immunogenic cell death in murine PCa cells and in TRAMP explants. The findings highlight that SP-D plays an anti-tumourigenic role in PCa by inducing immunogenic cell death and immunomodulation while the prostate tumour milieu adversely impacts SP-D by inhibiting its transcription, and enhancing its proteolytic degradation. Transformation of an immunologically “cold tumour” into a “hot tumour” implicates therapeutic potential of rfhSP-D in PCa.

The androgen receptor expression and its activity have different relationships with prognosis in hepatocellular carcinoma

The role of the Androgen Receptor (AR) expression and its activity in the prognosis of hepatocellular carcinoma (HCC) remains inconclusive. The aim of this study is to analyze the role of the AR expression and its activity as prognostic biomarkers in HCC. Three-hundred and thirty-seven patients from The Cancer Genome Atlas (TCGA) (107 females; 59.42 years [SD = 13.0]) were included. To infer AR activity, the expression-profile of previously validated androgen responsive genes (ARGs) was included. AR activity was shown by the AR-Score-21 (21 ARGs) and AR-Score-13 (13 ARGs) that were computed based on the expression of the selected ARGs. Those ARGs whose expression was significantly different between histological grades were used for computing two new AR-Scores. HCC patients with higher AR expression showed a higher median overall survival (OS). AR-Score 21 and AR-Score-13 did not show any association with prognosis. Six of the 21 ARGs of the AR-Score-21 and 7 of the 13 ARGs of the AR-Score-13 showed a significant different expression profile among histological grades. Based on these differences, another two AR-Scores were computed (AR-Score-6 and AR-Score-7). They showed the relative increase of upregulated to downregulated ARGs in high-grade HCC. Higher AR activity inferred by these AR-Scores was associated with worse outcomes. The expression of AR is associated with a better prognosis in HCC. However, the activity of the AR seems to be qualitatively different among histological grades. The AR activity inferred by the shifted ARGs is associated with a worse prognosis in HCC patients.

Effect of upregulation of DD3 on early detection and prognosis in prostate cancer

Background

Expression of prostate cancer antigen 3 (PCA3 OR DD3) in the blood has been reported to be significantly higher in prostate cancer (PCa) than in benign prostate hyperplasia (BPH). To confirm whether DD3 expression is significantly different between PCa and BPH tissues, DD3 expression was tested in the blood both preoperatively and postoperatively and in the paired tissues of PCa patients.

Methods

Expression levels of DD3 mRNA in the blood of patients who did not undergo surgery (PCa, n=102; BPH, n=53), those underwent surgery (preoperative, n=35; postoperative, n=35), and in PCa tissue specimens (tumor, n=41; adjacent normal, n=21) were determined by real-time quantitative PCR. Sensitivity and specificity for DD3 in PCa patients were validated by receiver operating characteristic (ROC) curve analysis.

Results

Our data suggest that expression level of DD3 in blood samples was significantly higher in PCa patients than in BPH patients (P=0.005). Expression of DD3 mRNA was also significantly elevated in PCa tissues compared with adjacent normal tissues (P=0.013). The increase in DD3 expression in PCa patients was further validated using a dataset from The Cancer Genome Atlas (n=549). Postoperative DD3 expression decreased following surgical intervention (P<0.001). Moreover, low DD3 expression was associated with improved overall survival (OS). Using gene set enrichment analysis, DD3 expression was correlated with specific PCa target genes including carcinogenesis-related and cancer proliferation-related genes.

Conclusions

This study demonstrated that expression of DD3 was upregulated in blood and PCa tumor tissues and was associated with prognosis. The oncogenic role of DD3 was further validated in the TCGA database, indicating that DD3 is a potential therapeutic target for PCa. Furthermore, this study suggests that DD3 expression could be considered as a prognostic biomarker for PCa.

Challenges in Cancer Biomarker Discovery Exemplified by the Identification of Diagnostic MicroRNAs in Prostate Tissues

Identification and clinical translation of routinely tested biomarkers require a complex and multistep workflow. Here, we described a confirmatory process estimating the utility of previously identified candidate tissue miRNAs for diagnosis of prostate cancer (PCa). RNA was isolated from formalin-fixed paraffin-embedded (FFPE) prostate tissue surgically resected from 44 patients with PCa and 24 patients with benign prostate hyperplasia (BPH). Of the 92 RNA samples obtained, 68 represented 42 malignant (PCa) areas and 26 represented nonmalignant (PCa 0%) areas of the prostate tissue sections. The levels of miR-32-5p, miR-183-5p, miR-141-5p, miR-187-3p, miR-375, miR-663b, miR-615-3p, miR-205-5p, miR-221-3p, and miR-222-3p were evaluated using Exiqon chemistry. Five (miR-32-5p, miR-141-5p, miR-187-3p, miR-375, and miR-615-3p), one (miR-32-5p), and two (miR-32-5p and miR-141-5p) miRNAs discriminated between BPH and areas of cancer-bearing prostate tissue harboring different numbers of cancer cells (PCa 15–70%, PCa 2–10%, and PCA 0%, respectively), with an area under the receiver operating characteristics curve (AUC-ROC) > 0.9. Only miRNA 32-5p discriminated BPH specimens from sections of cancer-bearing prostate tissue with a low percentage, a high percentage, or no dysplastic cells. miR-32-5p could be considered as potential diagnostic biomarker discriminating BPH from noncancerous areas within cancer-bearing prostate tissue. However, further clinical studies are warranted to confirm its diagnostic utility.

Plasma Messenger RNAs Identified Through Bioinformatics Analysis are Novel, Non-Invasive Prostate Cancer Biomarkers

Aim

To identify new biomarkers of prostate cancer (PCa) for the diagnosis and prediction of clinical outcomes.

Materials and Methods

Existing microarray data of PCa tissues in the Oncomine database were analyzed and candidate differentially expressed genes (DEGs) that may be novel and noninvasive biomarkers were obtained. On this basis, plasma mRNA was extracted from PCa patients and healthy donors. Furthermore, plasma mRNA expression of DEGs was evaluated by qRT-PCR. Finally, the diagnostic power of the biomarkers was evaluated in comparison to the clinical characteristics of the patients.

Results

In this study, the top five significantly overexpressed mRNA (AMACR, PPP1R14b, PCA3, DLX1, and RPL22L1) and the top five significantly underexpressed mRNA (DUOX1, EFS, GSTP1, S100A16, and NCRNA00087) were selected for further validation in PCa patients and healthy donors by qRT-PCR. The results showed that AMACR, DLX1, PCA3, DUOX1, and GSTP1 mRNA were stably amplified in plasma. Additionally, DLX1, PCA3, DUOX1, and GSTP1 mRNA expression was significantly different between PCa circulating free mRNA samples and healthy donors. These mRNAs may be useful biomarkers for PCa diagnosis.

Conclusion

Analysis of the expression of genes in the Oncomine database showed that DLX1, PCA3, and DUOX1 expressions have a cancer specific pattern in PCa. Collectively, DLX1, PCA3, and DUOX1 may be useful candidate biomarkers for PCa diagnosis.

Prognostic and diagnostic significance of Cavin 2 in lung adenocarcinoma

INTRODUCTION

Cavin 2 down-regulation is reported in several malignant tumors and is associated with tumor progression. However, the role of Cavin 2 in lung adenocarcinoma is unknown. This study aimed to investigate the prognostic and diagnostic significance of Cavin 2 in lung adenocarcinoma.

MATERIAL AND METHODS

Cavin 2 expression levels were examined in 150 cases of lung adenocarcinoma and matched adjacent normal lung tissues using RNA extraction and reverse transcription-quantitative polymerase chain reaction (RT-qPCR), Western blotting and immunohistochemistry (IHC) assays. Then, the relationship of Cavin 2 expression with clinicopathological characteristics and patients' survival was further evaluated in lung adenocarcinoma.

RESULTS

QPCR and Western blotting analysis indicated that Cavin 2 expression levels were significantly lower in lung adenocarcinoma tissues compared with those in adjacent normal lung tissues (p < 0.0001). The IHC results showed that positive expression of Cavin 2 was mainly located in cytoplasm as brown, but was hard to detect in lung adenocarcinoma tissues. The low-expression rates of Cavin 2 in lung adenocarcinoma and adjacent normal lung tissues were 62.0% and 20.0%, respectively, and the difference was significant (p < 0.0001). Lower expression of Cavin 2 was significantly associated with tumor size, TNM stage and lymph node metastasis (p < 0.05).

CONCLUSIONS

Cavin 2 has low expression in lung adenocarcinoma, which might be regarded as a potential prognostic and diagnostic biomarker.

Proteomics Analysis of Formalin Fixed Paraffin Embedded Tissues in the Investigation of Prostate Cancer

Prostate cancer (PCa) is one of the leading causes of death in men worldwide. The molecular features, associated with the onset and progression of the disease, are under vigorous investigation. Formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources for large-scale studies; however, their application in proteomics is limited due to protein cross-linking. In this study, the adjustment of a protocol for the proteomic analysis of FFPE tissues was performed which was followed by a pilot application on FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data for the investigation of PCa. For the optimization, FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization methods (beads, sonication, boiling) and buffers (SDS based and urea-thiourea based). The proteome extraction efficacy was then evaluated based on protein identifications and reproducibility using SDS electrophoresis and high resolution LC-MS/MS analysis. Comparison between the FFPE and matched fresh frozen (FF) tissues, using an optimized protocol involving protein extraction with an SDS-based buffer following beads homogenization and boiling, showed a substantial overlap in protein identifications with a strong correlation in relative abundances (r_s = 0.819, p < 0.001). Next, FFPE tissues (3 sections, 15 μm each per sample) from 10 patients with PCa corresponding to tumor (GS = 6 or GS ≥ 8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis. Proteins significantly deregulated between PCa GS ≥ 8 and PCa GS = 6 represented extracellular matrix organization, gluconeogenesis, and phosphorylation pathways. Proteins deregulated between cancerous and adjacent benign tissues, reflected increased translation, peptide synthesis, and protein metabolism in the former, which is consistent with the literature. In conclusion, the results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material.

Progression of the role of CRYAB in signaling pathways and cancers

CRYAB is a member of the small heat shock protein family, first discovered in the lens of the eye, and involved in various diseases, such as eye and heart diseases and even cancers, for example, breast cancer, lung cancer, prostate cancer, and ovarian cancer. In addition, CRYAB proteins are involved in a variety of signaling pathways including apoptosis, inflammation, and oxidative stress. This review summarizes the recent progress concerning the role of CRYAB in signaling pathways and diseases. Therefore, the role of CRYAB in signaling pathways and cancers is urgently needed. This article reviews the regulation of CRYAB in the apoptotic inflammatory signaling pathway and its role in cancers progression and as a key role in anti-cancer therapy targeting CRYAB in an effort to improve outcomes for patients with metastatic disease.

Integrative analysis of transcriptomics and clinical data uncovers the tumor-suppressive activity of MITF in prostate cancer

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

Gene expression feature selection for prostate cancer diagnosis using a two-phase heuristic-deterministic search strategy

Here, a two-phase search strategy is proposed to identify the biomarkers in gene expression data set for the prostate cancer diagnosis. A statistical filtering method is initially employed to remove the noisiest data. In the first phase of the search strategy, a multi-objective optimisation based on the binary particle swarm optimisation algorithm tuned by a chaotic method is proposed to select the optimal subset of genes with the minimum number of genes and the maximum classification accuracy. Finally, in the second phase of the search strategy, the cache-based modification of the sequential forward floating selection algorithm is used to find the most discriminant genes from the optimal subset of genes selected in the first phase. The results of applying the proposed algorithm on the available challenging prostate cancer data set demonstrate that the proposed algorithm can perfectly identify the informative genes such that the classification accuracy, sensitivity, and specificity of 100% are achieved with only nine biomarkers.

Transcriptional Repression and Protein Degradation of the Ca2+-Activated K+ Channel KCa1.1 by Androgen Receptor Inhibition in Human Breast Cancer Cells

The large-conductance Ca²⁺-activated K⁺ channel K_Ca1.1 plays an important role in the promotion of breast cancer cell proliferation and metastasis. The androgen receptor (AR) is proposed as a therapeutic target for AR-positive advanced triple-negative breast cancer. We herein investigated the effects of a treatment with antiandrogens on the functional activity, activation kinetics, transcriptional expression, and protein degradation of K_Ca1.1 in human breast cancer MDA-MB-453 cells using real-time PCR, Western blotting, voltage-sensitive dye imaging, and whole-cell patch clamp recording. A treatment with the antiandrogen bicalutamide or enzalutamide for 48 h significantly suppressed (1) depolarization responses induced by paxilline (PAX), a specific K_Ca1.1 blocker and (2) PAX-sensitive outward currents induced by the depolarizing voltage step. The expression levels of K_Ca1.1 transcripts and proteins were significantly decreased in MDA-MB-453 cells, and the protein degradation of K_Ca1.1 mainly contributed to reductions in K_Ca1.1 activity. Among the eight regulatory β and γ subunits, LRRC26 alone was expressed at high levels in MDA-MB-453 cells and primary and metastatic breast cancer tissues, whereas no significant changes were observed in the expression levels of LRRC26 and activation kinetics of PAX-sensitive outward currents in MDA-MB-453 cells by the treatment with antiandrogens. The treatment with antiandrogens up-regulated the expression of the ubiquitin E3 ligases, FBW7, MDM2, and MDM4 in MDA-MB-453 cells, and the protein degradation of K_Ca1.1 was significantly inhibited by the respective siRNA-mediated blockade of FBW7 and MDM2. Based on these results, we concluded that K_Ca1.1 is an androgen-responsive gene in AR-positive breast cancer cells, and its down-regulation through enhancements in its protein degradation by FBW7 and/or MDM2 may contribute, at least in part, to the antiproliferative and antimetastatic effects of antiandrogens in breast cancer cells.

RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers

BACKGROUND

Current -omics technologies are able to sense the state of a biological sample in a very wide variety of ways. Given the high dimensionality that typically characterises these data, relevant knowledge is often hidden and hard to identify. Machine learning methods, and particularly feature selection algorithms, have proven very effective over the years at identifying small but relevant subsets of variables from a variety of application domains, including -omics data. Many methods exist with varying trade-off between the size of the identified variable subsets and the predictive power of such subsets. In this paper we focus on an heuristic for the identification of biomarkers called RGIFE: Rank Guided Iterative Feature Elimination. RGIFE is guided in its biomarker identification process by the information extracted from machine learning models and incorporates several mechanisms to ensure that it creates minimal and highly predictive features sets.

RESULTS

We compare RGIFE against five well-known feature selection algorithms using both synthetic and real (cancer-related transcriptomics) datasets. First, we assess the ability of the methods to identify relevant and highly predictive features. Then, using a prostate cancer dataset as a case study, we look at the biological relevance of the identified biomarkers.

CONCLUSIONS

We propose RGIFE, a heuristic for the inference of reduced panels of biomarkers that obtains similar predictive performance to widely adopted feature selection methods while selecting significantly fewer feature. Furthermore, focusing on the case study, we show the higher biological relevance of the biomarkers selected by our approach. The RGIFE source code is available at: http://ico2s.org/software/rgife.html .

Trends in Gene Expression Profiling for Prostate Cancer Risk Assessment: A Systematic Review

Objectives

The aim of the study is to review biotechnology advances in gene expression profiling on prostate cancer (PCa), focusing on experimental platform development and gene discovery, in relation to different study designs and outcomes in order to understand how they can be exploited to improve PCa diagnosis and clinical management.

Methods

We conducted a systematic literature review on gene expression profiling studies through PubMed/MEDLINE and Web of Science between 2000 and 2016. Tissue biopsy and clinical gene profiling studies with different outcomes (e.g., recurrence, survival) were included.

Results

Over 3,000 papers were screened and 137 full-text articles were selected. In terms of technology used, microarray is still the most popular technique, increasing from 50 to 70% between 2010 and 2015, but there has been a rise in the number of studies using RNA sequencing (13% in 2015). Sample sizes have increased, as well as the number of genes that can be screened all at once, but we have also observed more focused targeting in more recent studies. Qualitative analysis on the specific genes found associated with PCa risk or clinical outcomes revealed a large variety of gene candidates, with a few consistent cross-studies.

Conclusions

The last 15 years of research in gene expression in PCa have brought a large volume of data and information that has been decoded only in part, but advancements in high-throughput sequencing technology are increasing the amount of data that can be generated. The variety of findings warrants the execution of both validation studies and meta-analyses. Genetic biomarkers have tremendous potential for early diagnosis of PCa and, if coupled with other diagnostics (e.g., imaging), can effectively be used to concretize less-invasive, personalized prediction of PCa risk and progression.

Infiltrating Myeloid Cells Exert Protumorigenic Actions via Neutrophil Elastase

Tissue infiltration and elevated peripheral circulation of granulocytic myeloid-derived cells is associated with poor outcomes in prostate cancer and other malignancies. Although myeloid-derived cells have the ability to suppress T-cell function, little is known about the direct impact of these innate cells on prostate tumor growth. Here, it is reported that granulocytic myeloid-derived suppressor cells (MDSC) are the predominant tumor-infiltrating cells in prostate cancer xenografts established in athymic nude mice. MDSCs significantly increased in number in the peripheral circulation as a function of xenograft growth and were successfully depleted in vivo by Gr-1 antibody treatment. Importantly, MDSC depletion significantly decreased xenograft growth. We hypothesized that granulocytic MDSCs might exert their protumorigenic actions in part through neutrophil elastase (ELANE), a serine protease released upon granulocyte activation. Indeed, it was determined that NE is expressed by infiltrating immune cells and is enzymatically active in prostate cancer xenografts and in prostate tumors of prostate-specific Pten-null mice. Importantly, treatment with sivelestat, a small-molecule inhibitor specific for NE, significantly decreased xenograft growth, recapitulating the phenotype of Gr-1 MDSC depletion. Mechanistically, NE activated MAPK signaling and induced MAPK-dependent transcription of the proliferative gene cFOS in prostate cancer cells. Functionally, NE stimulated proliferation, migration, and invasion of prostate cancer cells in vitro IHC on human prostate cancer clinical biopsies revealed coexpression of NE and infiltrating CD33⁺ MDSCs.Implications: This report suggests that MDSCs and NE are physiologically important mediators of prostate cancer progression and may serve as potential biomarkers and therapeutic targets. Mol Cancer Res; 15(9); 1138-52. ©2017 AACR.

Stably Expressed Genes Involved in Basic Cellular Functions

Stably Expressed Genes (SEGs) whose expression varies within a narrow range may be involved in core cellular processes necessary for basic functions. To identify such genes, we re-analyzed existing RNA-Seq gene expression profiles across 11 organs at 4 developmental stages (from immature to old age) in both sexes of F344 rats (n = 4/group; 320 samples). Expression changes (calculated as the maximum expression / minimum expression for each gene) of >19000 genes across organs, ages, and sexes ranged from 2.35 to >10⁹-fold, with a median of 165-fold. The expression of 278 SEGs was found to vary ≤4-fold and these genes were significantly involved in protein catabolism (proteasome and ubiquitination), RNA transport, protein processing, and the spliceosome. Such stability of expression was further validated in human samples where the expression variability of the homologous human SEGs was significantly lower than that of other genes in the human genome. It was also found that the homologous human SEGs were generally less subject to non-synonymous mutation than other genes, as would be expected of stably expressed genes. We also found that knockout of SEG homologs in mouse models was more likely to cause complete preweaning lethality than non-SEG homologs, corroborating the fundamental roles played by SEGs in biological development. Such stably expressed genes and pathways across life-stages suggest that tight control of these processes is important in basic cellular functions and that perturbation by endogenous (e.g., genetics) or exogenous agents (e.g., drugs, environmental factors) may cause serious adverse effects.

Validation of Novel Biomarkers for Prostate Cancer Progression by the Combination of Bioinformatics, Clinical and Functional Studies

The identification and validation of biomarkers for clinical applications remains an important issue for improving diagnostics and therapy in many diseases, including prostate cancer. Gene expression profiles are routinely applied to identify diagnostic and predictive biomarkers or novel targets for cancer. However, only few predictive markers identified in silico have also been validated for clinical, functional or mechanistic relevance in disease progression. In this study, we have used a broad, bioinformatics-based approach to identify such biomarkers across a spectrum of progression stages, including normal and tumor-adjacent, premalignant, primary and late stage lesions. Bioinformatics data mining combined with clinical validation of biomarkers by sensitive, quantitative reverse-transcription PCR (qRT-PCR), followed by functional evaluation of candidate genes in disease-relevant processes, such as cancer cell proliferation, motility and invasion. From 300 initial candidates, eight genes were selected for validation by several layers of data mining and filtering. For clinical validation, differential mRNA expression of selected genes was measured by qRT-PCR in 197 clinical prostate tissue samples including normal prostate, compared against histologically benign and cancerous tissues. Based on the qRT-PCR results, significantly different mRNA expression was confirmed in normal prostate versus malignant PCa samples (for all eight genes), but also in cancer-adjacent tissues, even in the absence of detectable cancer cells, thus pointing to the possibility of pronounced field effects in prostate lesions. For the validation of the functional properties of these genes, and to demonstrate their putative relevance for disease-relevant processes, siRNA knock-down studies were performed in both 2D and 3D organotypic cell culture models. Silencing of three genes (DLX1, PLA2G7 and RHOU) in the prostate cancer cell lines PC3 and VCaP by siRNA resulted in marked growth arrest and cytotoxicity, particularly in 3D organotypic cell culture conditions. In addition, silencing of PLA2G7, RHOU, ACSM1, LAMB1 and CACNA1D also resulted in reduced tumor cell invasion in PC3 organoid cultures. For PLA2G7 and RHOU, the effects of siRNA silencing on proliferation and cell-motility could also be confirmed in 2D monolayer cultures. In conclusion, DLX1 and RHOU showed the strongest potential as useful clinical biomarkers for PCa diagnosis, further validated by their functional roles in PCa progression. These candidates may be useful for more reliable identification of relapses or therapy failures prior to the recurrence local or distant metastases.

Ion channel expression as promising cancer biomarker

Cancer is a disease with marked heterogeneity in both response to therapy and survival. Clinical and histopathological characteristics have long determined prognosis and therapy. The introduction of molecular diagnostics has heralded an explosion in new prognostic factors. Overall, histopathology, immunohistochemistry and molecular biology techniques have described important new prognostic subgroups in the different cancer categories. Ion channels and transporters (ICT) are a new class of membrane proteins which are aberrantly expressed in several types of human cancers. Besides regulating different aspect of cancer cell behavior, ICT can now represent novel cancer biomarkers. A summary of the data obtained so far and relative to breast, prostate, lung, colorectal, esophagus, pancreatic and gastric cancers are reported. Special emphasis is given to those studies aimed at relating specific ICT or a peculiar ICT profile with current diagnostic methods. Overall, we are close to exploit ICTs for diagnostic, prognostic or predictive purposes in cancer. This article is part of a Special Issue entitled: Membrane channels and transporters in cancers.

DNA methylation status is more reliable than gene expression at detecting cancer in prostate biopsy

Background:

We analysed critically the potential usefulness of RNA- and DNA-based biomarkers in supporting conventional histological diagnostic tests for prostate carcinoma (PCa) detection.

Methods:

Microarray profiling of gene expression and DNA methylation was performed on 16 benign prostatic hyperplasia (BPH) and 32 cancerous and non-cancerous prostate samples extracted by radical prostatectomy. The predictive value of the selected biomarkers was validated by qPCR-based methods using tissue samples extracted from the 58 prostates and, separately, using 227 prostate core biopsies.

Results:

HOXC6, AMACR and PCA3 expression showed the best discrimination between PCa and BPH. All three genes were previously reported as the most promising mRNA-based markers for distinguishing cancerous lesions from benign prostate lesions; however, none were sufficiently sensitive and specific to meet the criteria for a PCa diagnostic biomarker. By contrast, DNA methylation levels of the APC, TACC2, RARB, DGKZ and HES5 promoter regions achieved high discriminating sensitivity and specificity, with area under the curve (AUCs) reaching 0.95−1.0. Only a small overlap was detected between the DNA methylation levels of PCa-positive and PCa-negative needle biopsies, with AUCs ranging between 0.854 and 0.899.

Conclusions:

DNA methylation-based biomarkers reflect the prostate malignancy and might be useful in supporting clinical decisions for suspected PCa following an initial negative prostate biopsy.

Condition-specific target prediction from motifs and expression

MOTIVATION

It is commonplace to predict targets of transcription factors (TFs) by sequence matching with their binding motifs. However, this ignores the particular condition of the cells. Gene expression data can provide condition-specific information, as is, e.g. exploited in Motif Enrichment Analysis.

RESULTS

Here, we introduce a novel tool named condition-specific target prediction (CSTP) to predict condition-specific targets for TFs from expression data measured by either microarray or RNA-seq. Based on the philosophy of guilt by association, CSTP infers the regulators of each studied gene by recovering the regulators of its co-expressed genes. In contrast to the currently used methods, CSTP does not insist on binding sites of TFs in the promoter of the target genes. CSTP was applied to three independent biological processes for evaluation purposes. By analyzing the predictions for the same TF in three biological processes, we confirm that predictions with CSTP are condition-specific. Predictions were further compared with true TF binding sites as determined by ChIP-seq/chip. We find that CSTP predictions overlap with true binding sites to a degree comparable with motif-based predictions, although the two target sets do not coincide.

AVAILABILITY AND IMPLEMENTATION

CSTP is available via a web-based interface at http://cstp.molgen.mpg.de.