A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

Detection of differentially methylated CpGs between tumour and adjacent benign cells in diagnostic prostate cancer samples

Differentially methylated CpG sites (dmCpGs) that distinguish prostate tumour from adjacent benign tissue could aid in the diagnosis and prognosis of prostate cancer. Previously, the identification of such dmCpGs has only been undertaken in radical prostatectomy (RP) samples and not primary diagnostic tumour samples (needle biopsy or transurethral resection of the prostate). We interrogated an Australian dataset comprising 125 tumour and 43 adjacent histologically benign diagnostic tissue samples, including 41 paired samples, using the Infinium Human Methylation450 BeadChip. Regression analyses of paired tumour and adjacent benign samples identified 2,386 significant dmCpGs (Bonferroni p < 0.01; delta-β ≥ 40%), with LASSO regression selecting 16 dmCpGs that distinguished tumour samples in the full Australian diagnostic dataset (AUC = 0.99). Results were validated in independent North American (npaired = 19; AUC = 0.87) and The Cancer Genome Atlas (TCGA; npaired = 50; AUC = 0.94) RP datasets. Two of the 16 dmCpGs were in genes that were significantly down-regulated in Australian tumour samples (Bonferroni p < 0.01; GSTM2 and PRKCB). Ten additional dmCpGs distinguished low (n = 34) and high Gleason (n = 88) score tumours in the diagnostic Australian dataset (AUC = 0.95), but these performed poorly when applied to the RP datasets (North American: AUC = 0.66; TCGA: AUC = 0.62). The DNA methylation marks identified here could augment and improve current diagnostic tests and/or form the basis of future prognostic tests.

Epigenetic regulation of androgen dependent and independent prostate cancer

Prostate cancer is one of the most common malignancies among men worldwide. Besides genetic alterations, epigenetic modulations including DNA methylation, histone modifications and miRNA mediated alteration of gene expression are the key driving forces for the prostate tumor development and cancer progression. Aberrant expression and/or the activity of the epigenetic modifiers/enzymes, results in aberrant expression of genes involved in DNA repair, cell cycle regulation, cell adhesion, apoptosis, autophagy, tumor suppression and hormone response and thereby disease progression. Altered epigenome is associated with prostate cancer recurrence, progression, aggressiveness and transition from androgen-dependent to androgen-independent phenotype. These epigenetic modifications are reversible and various compounds/drugs targeting the epigenetic enzymes have been developed that are effective in cancer treatment. This chapter focuses on the epigenetic alterations in prostate cancer initiation and progression, listing different epigenetic biomarkers for diagnosis and prognosis of the disease and their potential as therapeutic targets. This chapter also summarizes different epigenetic drugs approved for prostate cancer therapy and the drugs available for clinical trials.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Regulatory network and targeted interventions for CCDC family in tumor pathogenesis

CCDC (coiled-coil domain-containing) is a coiled helix domain that exists in natural proteins. There are about 180 CCDC family genes, encoding proteins that are involved in intercellular transmembrane signal transduction and genetic signal transcription, among other functions. Alterations in expression, mutation, and DNA promoter methylation of CCDC family genes have been shown to be associated with the pathogenesis of many diseases, including primary ciliary dyskinesia, infertility, and tumors. In recent studies, CCDC family genes have been found to be involved in regulation of growth, invasion, metastasis, chemosensitivity, and other biological behaviors of malignant tumor cells in various cancer types, including nasopharyngeal carcinoma, lung cancer, colorectal cancer, and thyroid cancer. In this review, we summarize the involvement of CCDC family genes in tumor pathogenesis and the relevant upstream and downstream molecular mechanisms. In addition, we summarize the potential of CCDC family genes as tumor therapy targets. The findings discussed here help us to further understand the role and the therapeutic applications of CCDC family genes in tumors.

Epigenetic (De)regulation in Prostate Cancer

Prostate cancer (PCa) is a heterogeneous disease exhibiting both genetic and epigenetic deregulations. Epigenetic alterations are defined as changes not based on DNA sequence, which include those of DNA methylation, histone modification, and chromatin remodeling. Androgen receptor (AR) is the main driver for PCa and androgen deprivation therapy (ADT) remains a backbone treatment for patients with PCa; however, ADT resistance almost inevitably occurs and advanced diseases develop termed castration-resistant PCa (CRPC), due to both genetic and epigenetic changes. Due to the reversible nature of epigenetic modifications, inhibitors targeting epigenetic factors have become promising anti-cancer agents. In this chapter, we focus on recent studies about the dysregulation of epigenetic regulators crucially involved in the initiation, development, and progression of PCa and discuss the potential use of inhibitors targeting epigenetic modifiers for treatment of advanced PCa.

Future directions for precision oncology in prostate cancer

Clinical genomic testing is becoming routine in prostate cancer, as biomarker-driven therapies such as poly-ADP ribose polymerase (PARP) inhibitors and anti-PD1 immunotherapy are now approved for select men with castration-resistant prostate cancer harboring alterations in DNA repair genes. Challenges for precision medicine in prostate cancer include an overall low prevalence of actionable genomic alterations and a still limited understanding of the impact of tumor heterogeneity and co-occurring alterations on treatment response and outcomes across diverse patient populations. Expanded tissue-based technologies such as whole-genome sequencing, transcriptome analysis, epigenetic analysis, and single-cell RNA sequencing have not yet entered the clinical realm and could potentially improve upon our understanding of how molecular features of tumors, intratumoral heterogeneity, and the tumor microenvironment impact therapy response and resistance. Blood-based technologies including cell-free DNA, circulating tumor cells (CTCs), and extracellular vesicles (EVs) are less invasive molecular profiling resources that could also help capture intraindividual tumor heterogeneity and track dynamic changes that occur in the context of specific therapies. Furthermore, molecular imaging is an important biomarker tool within the framework of prostate cancer precision medicine with a capability to detect heterogeneity across metastases and potential therapeutic targets less invasively. Here, we review recent technological advances that may help promote the future implementation and value of precision oncology testing for patients with advanced prostate cancer.

Systematic Elucidation of the Aneuploidy Landscape and Identification of Aneuploidy Driver Genes in Prostate Cancer

Aneuploidy is widely identified as a remarkable feature of malignancy genomes. Increasing evidences suggested aneuploidy was involved in the progression and metastasis of prostate cancer (PCa). Nevertheless, no comprehensive analysis was conducted in PCa about the effects of aneuploidy on different omics and, especially, about the driver genes of aneuploidy. Here, we validated the association of aneuploidy with the progression and prognosis of PCa and performed a systematic analysis in mutation profile, methylation profile, and gene expression profile, which detailed the molecular process aneuploidy implicated. By multi-omics analysis, we managed to identify 11 potential aneuploidy driver genes (GSTM2, HAAO, C2orf88, CYP27A1, FAXDC2, HFE, C8orf88, GSTP1, EFS, HIF3A, and WFDC2), all of which were related to the development and metastasis of PCa. Meanwhile, we also found aneuploidy and its driver genes were correlated with the immune microenvironment of PCa. Our findings could shed light on the tumorigenesis of PCa and provide a better understanding of the development and metastasis of PCa; additionally, the driver genes could be promising and actionable therapeutic targets pointing to aneuploidy.

Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

Screening to Identify an Immune Landscape-Based Prognostic Predictor and Therapeutic Target for Prostate Cancer

Objectives

Existing prognostic risk assessment strategies for prostate cancer (PCa) remain unsatisfactory. Similar treatments for patients at the same disease stage can lead to different survival outcomes. Thus, we aimed to explore a novel immune landscape-based prognostic predictor and therapeutic target for PCa patients.

Methods

A total of 490 PCa patients from The Cancer Genome Atlas Project (TCGA) cohort were analyzed to obtain immune landscape-based prognostic features. Then, analyses at different levels were performed to explore the relevant survival mechanisms, prognostic predictors, and therapeutic targets. Finally, experimental verification was performed using a tissue microarray (TMA) from 310 PCa patients. Furthermore, a nomogram was constructed to provide a quantitative approach for predicting the prognosis of patients with PCa.

Results

The immune landscape-based risk score (ILBRS) was obtained. Then, VAV1, which presented a significant positive correlation with Treg infiltration and ILBRS, was screened and identified to be significantly related to the prognosis of PCa. Finally, experimental verification confirmed the prognostic value of VAV1 for PCa prognosis at the protein level.

Conclusions

VAV1 has the potential to be developed as an immune landscape-based PCa prognostic predictor and therapeutic target and will help improve prognosis by enabling the selection of individualized, targeted therapy.

Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

Background

Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns.

Results

We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1).

Conclusions

Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04416-w.

Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma

Background

Multiomics data analysis based on high-throughput sequencing technology has become a hotspot in tumor investigation. The present study aimed to explore prognostic biomarkers via investigating DNA copy number variation (CNV) and methylation variation (MET) data in prostate cancer.

Methods

We obtained the messenger RNA (mRNA) expression, CNV, and methylated data of prostate adenocarcinoma (PRAD) samples via The Cancer Genome Atlas (TCGA)-PRAD cohort. We calculated and assessed the associations between CNV and RNA sequencing (RNA-seq), and between MET and RNA-seq via Pearson correlation coefficients. We then used the "iCluster" package to perform multigroup cluster analysis with CNVcor gene CNV data, METcor gene methylation data, and CNVcor and METcor gene mRNA data. The univariate Cox analysis was used to screen significant hub genes, and multivariate Cox analysis was used to construct risk a model. The nomogram was constructed based on "rms" package, and the immune infiltrating patterns were compared between high- and low-risk groups.

Results

A total of 477 PRAD samples with complete CNV, methylation, mRNA, and matched clinical information were included in our study. A list of 10,073 CNVcor genes and 9841 METcor genes were confirmed with a significance level of P<0.01. We found that CNVcor is more likely to appear on chromosome (chr)8, chr17, and chr10, while METcor is more likely to appear on chr1, chr19, and chr17. Based on the core genes, we finally classified the samples into 4 subtypes, incorporating iC1 (iCluster) (92 samples), iC2 (79 samples), iC3 (165 samples), and iC4 (141 samples). Furthermore, we constructed the prognostic model for PRAD based on the 5 genes (IER3, AOX1, PRKCDBP, UBD, and FBLN5). Nomograms incorporating risk score and other clinical variables were further constructed, and these nomograms exhibited superior predictive ability. We further compared the differential immune infiltrating patterns in 2 risk groups and found significantly low levels of infiltrating cluster of differentiation (CD)8⁺ T cells in high-risk samples.

Conclusions

Our study integrated the multi-omics data to elucidate the molecular features of PRAD and pivotal genes for predicting prognosis.

GSTP1 as a novel target in radiation induced lung injury

The glutathione S-transferase P1(GSTP1) is an isoenzyme in the glutathione-S transferases (GSTs) enzyme system, which is the most abundant GSTs expressed in adult lungs. Recent research shows that GSTP1 is closely related to the regulation of cell oxidative stress, inhibition of cell apoptosis and promotion of cytotoxic metabolism. Interestingly, there is evidence that GSTP1 single nucleotide polymorphisms (SNP) 105Ile/Val related to the risk of radiation induced lung injury (RILI) development, which strongly suggests that GSTP1 is closely associated with the occurrence and development of RILI. In this review, we discuss our understanding of the role of GSTP1 in RILI and its possible mechanism.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Screening and identification of Theileria annulata subtelomere-encoded variable secreted protein-950454 (SVSP454) interacting proteins from bovine B cells

BACKGROUND

Theileria annulata is a protozoan parasite that can infect and transform bovine B cells, macrophages, and dendritic cells. The mechanism of the transformation is still not well understood, and some parasite molecules have been identified, which contribute to cell proliferation by regulating host signaling pathways. Subtelomeric variable secreted proteins (SVSPs) of Theileria might affect the host cell phenotype, but its function is still not clear. Therefore, in the present study, we explored the interactions of SVSP454 with host cell proteins to investigate the molecular mechanism of T. annulata interaction with host cells.

METHODS

The transcription level of an SVSP protein from T. annulata, SVSP454, was analyzed between different life stages and transformed cell passages using qRT-PCR. Then, SVSP454 was used as a bait to screen its interacting proteins from the bovine B cell cDNA library using a yeast two-hybrid (Y2H) system. The potential interacting proteins of host cells with SVSP454 were further identified by using a coimmunoprecipitation (Co-IP) and bimolecular fluorescence complementation (BiFC) assays.

RESULTS

SVSP454 was transcribed in all three life stages of T. annulata but had the highest transcription during the schizont stage. However, the transcription level of SVSP454 continuously decreased as the cultures passaged. Two proteins, Bos Taurus coiled-coil domain 181 (CCDC181) and Bos Taurus mitochondrial ribosomal protein L30 (MRPL30), were screened. The proteins CCDC181 and MRPL30 of the host were further identified to directly interact with SVSP454.

CONCLUSION

In the present study, SVSP454 was used as a bait plasmid, and its prey proteins CCDC181 and MRPL30 were screened out by using a Y2H system. Then, we demonstrated that SVSP454 directly interacted with both CCDC181 and MRPL30 by Co-IP and BiFC assays. Therefore, we speculate that SVSP454-CCDC181/SVSP454MRPL30 is an interacting axis that regulates the microtubule network and translation process of the host by some vital signaling molecules. Identification of the interaction of SVSP454 with CCDC181 and MRPL30 will help illustrate the transformation mechanisms induced by T. annulata.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review

BACKGROUND

Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.

OBJECTIVE

This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice.

RESULTS

In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.

CONCLUSIONS

The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Recent Advances in Epigenetic Biomarkers and Epigenetic Targeting in Prostate Cancer

CONTEXT

In addition to genetic alterations, epigenetic alterations play a crucial role during prostate cancer progression. A better understanding of the epigenetic factors that promote prostate cancer progression may lead to the design of rational therapeutic strategies to target prostate cancer more effectively.

OBJECTIVE

To systematically review recent literature on the role of epigenetic factors in prostate cancer and highlight key preclinical and translational data with epigenetic therapies.

EVIDENCE ACQUISITION

We performed a systemic literature search in PubMed. At the request of the editors, we limited our search to articles published between January 2015 and August 2020 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Clinical trials targeting epigenetic factors were retrieved from clinicaltrials.gov.

EVIDENCE SYNTHESIS

We retrieved 1451 articles, and 62 were finally selected for review. Twelve additional foundational studies outside this time frame were also included. Findings from both preclinical and clinical studies were reviewed and summarized. We also discuss 12 ongoing clinical studies with epigenetic targeted therapies.

CONCLUSIONS

Epigenetic mechanisms impact prostate cancer progression. Understanding the role of specific epigenetic factors is critical to determine how we may improve prostate cancer treatment and modulate resistance to standard therapies. Recent preclinical studies and ongoing or completed clinical studies with epigenetic therapies provide a useful roadmap for how to best deploy epigenetic therapies clinically to target prostate cancer.

PATIENT SUMMARY

Epigenetics is a process by which gene expression is regulated without changes in the DNA sequence itself. Oftentimes, epigenetic changes influence cellular behavior and contribute to cancer development or progression. Understanding how epigenetic changes occur in prostate cancer is the first step toward therapeutic targeting in patients. Importantly, laboratory-based studies and recently completed and ongoing clinical trials suggest that drugs targeting epigenetic factors are promising. More work is necessary to determine whether this class of drugs will add to our existing treatment arsenal in prostate cancer.

Epigenetic reprogramming during prostate cancer progression: A perspective from development

Conrad Waddington's theory of epigenetic landscape epitomize the process of cell fate and cellular decision-making during development. Wherein the epigenetic code maintains patterns of gene expression in pluripotent and differentiated cellular states during embryonic development and differentiation. Over the years disruption or reprogramming of the epigenetic landscape has been extensively studied in the course of cancer progression. Cellular dedifferentiation being a key hallmark of cancer allow us to take cues from the biological processes involved during development. Here, we discuss the role of epigenetic landscape and its modifiers in cell-fate determination, differentiation and prostate cancer progression. Lately, the emergence of RNA-modifications has also furthered our understanding of epigenetics in cancer. The overview of the epigenetic code regulating androgen signalling, and progression to aggressive neuroendocrine stage of PCa reinforces its gene regulatory functions during the development of prostate gland as well as cancer progression. Additionally, we also highlight the clinical implications of cancer cell epigenome, and discuss the recent advancements in the therapeutic strategies targeting the advanced stage disease.

Leveraging the urban-rural divide for epigenetic research

Urbanization coincides with a complex change in environmental exposure and a rapid increase in noncommunicable diseases (NCDs). Epigenetics, including DNA methylation (DNAm), is thought to mediate part of the association between genetic/environmental exposure and NCDs. The urban-rural divide provides a unique opportunity to investigate the effect of the combined presence of multiple forms of environmental exposure on DNAm and the related increase in disease risk. This review evaluates the ability of three epidemiological study designs (migration, income-comparative and urban-rural designs) to investigate the role of DNAm in the association between urbanization and the rise in NCD prevalence. We also discuss the ability of each study design to address the gaps in the current literature, including the complex methylation-mediated risk attributable to the cluster of forms of exposure characterizing urban and rural living, while providing a platform for developing countries to leverage their demographic discrepancies in future research ventures.

Overexpression of GAS6 Promotes Cell Proliferation and Invasion in Bladder Cancer by Activation of the PI3K/AKT Pathway

Background

Growth arrest-specific 6 (GAS6) is a secreted vitamin K-dependent protein abnormally expressed in various human tumor tissues and can activate the receptor Tyro3, Axl, and Mer to promote cancer cell proliferation and invasion. Until now, the role of GAS6 has been poorly understood in bladder cancer (BCa).

Materials and Methods

Using bioinformatics analysis, we screened genes significantly associated with overall survival in BCa. The association between GAS6 and survival was evaluated by tissue microarray and IHC staining. We investigated the effect of GAS6 on the development of BCa through in vitro and in vivo experiments.

Results

Here, we report that GAS6 is highly expressed in bladder cancer and is significantly associated with tumor grade, T stage, and worse prognosis. We found that GAS6 depletion inhibited proliferation, migration, and invasion of BCa cells. In addition, bioinformatics analysis revealed that GAS6 may be involved in the regulation of PI3K-AKT signaling pathway by binding to receptor TAM and has a significant positive correlation with PI3K family gene expression. Furthermore, Western blot experiments have shown that GAS6 might modulate the PI3K-AKT signaling to regulate proliferation and invasion of BCa cells. Treatment of BCa cells with SC79, an AKT activator, partially restored the effect of GAS6 silencing on cell proliferation and invasion.

Conclusion

The present study suggests that GAS6 may play a pivotal role in the development of BCa and may be a potential target for its treatment.

The Effect of Hormone Therapy on the Expression of Prostate Cancer and Multi-Epigenetic Marker Genes in a Population of Iranian Patients

Background and Aim

Many recent studies have shown a direct relationship between the decrease in the expression of GSTP1 and RASSF1 with the incidence and progression of prostate cancer. Moreover, the expression level of these genes is greatly affected by epigenetic factors and their methylation pattern. Given the prevalence of prostate cancer and the importance of choosing the best method to inhibit the progression of the disease and provide specific treatment, it is important to evaluate the effect of hormone therapy on the expression of effective prostate cancer genes and epigenetic markers.

Patients and Methods

In this case-control study, 35 prostate cancer samples were examined before and after hormone therapy. Following the blood sampling, RNA extraction, and cDNA synthesis, the expression of GSTP1, RASSF1, HDAC, DNMT3A, and DNMT3B was assessed by real-time PCR.

Results

The results analysis showed that the expression of GSTP1, RASSF1, and DNMT3B was significantly increased, DNMT3A was significantly decreased (P value<0.05) and HDAC expression did not change significantly (P value=0.19) after hormone therapy.

Discussion

Significant changes in the expression of GSTP1, RASSF1, DNMT3B and DNMT3A in the studied samples indicate that these genes are susceptible targets for cancer hormone therapy in Iranian men like in the other populations. Evaluation of gene activity in a larger population of patients may support these findings.

The value of endothelin receptor type B promoter methylation as a biomarker for the risk assessment and diagnosis of prostate cancer: A meta-analysis

Previous researches have demonstrated that the methylation status of the EDNRB promoter was associated with the prostate cancer (PCa), but these conclusions remained controversial. Thus, the aim of this meta-analysis was to evaluate the association between EDNRB promoter methylation and the PCa. According to the PRISMA statement, the Web of Science, PubMed, EMBASE, and Cochrane Library databases were retrieved. The ORs and 95 % CIs were analyzed to evaluate the associations between EDNRB promoter methylation and the risk and clinical features of PCa. Heterogeneity among the included studies was estimated by I² statistic and Q test. Publication bias and sensitivity analysis were utilized to test the robustness of our outcomes. In addition, the pooled sensitivity and specificity were calculated to assess the diagnostic value of EDNRB methylation for PCa. Ultimately, 11 eligible studies were included. Under the random-effects model, the pooled OR shown that the frequency of EDNRB methylation was substantially higher in cases compared with controls (OR = 5.42, 95 % CI = 1.98-14.88, P = 0.001). The similar results were also found by the data from TCGA database. Subgroup analysis according to the methylation detection method showed that the heterogeneity in quantitative methylation-specific polymerase chain reaction (qMSP) group was insignificant (I² = 0.0 %, P = 0.669). Moreover, the pooled sensitivity for all-inclusive studies was 0.55 (95 % CI: 0.26-0.81), and the pooled specificity was 0.93 (95 % CI: 0.55-0.99). The methylation of EDNRB promoter might increase the risk of PCa. Meanwhile, EDNRB promoter methylation test combined with PSA testing and/or other biomarkers could be promising diagnostic biomarkers for more accurate detection of PCa.