Prognostic DNA methylation markers for prostate cancer. Int J Mol Sci. 2014;15:16544-16576. [PMID: 25238417 PMCID: PMC4200823 DOI: 10.3390/ijms150916544]

Hyaluronic Acid Interacting Molecules Mediated Crosstalk between Cancer Cells and Microenvironment from Primary Tumour to Distant Metastasis

Hyaluronic acid (HA) is a prominent component of the extracellular matrix, and its interactions with HA-interacting molecules (HAIMs) play a critical role in cancer development and disease progression. This review explores the multifaceted role of HAIMs in the context of cancer, focusing on their influence on disease progression by dissecting relevant cellular and molecular mechanisms in tumour cells and the tumour microenvironment. Cancer progression can be profoundly affected by the interactions between HA and HAIMs. They modulate critical processes such as cell adhesion, migration, invasion, and proliferation. The TME serves as a dynamic platform in which HAIMs contribute to the formation of a unique niche. The resulting changes in HA composition profoundly influence the biophysical properties of the TME. These modifications in the TME, in conjunction with HAIMs, impact angiogenesis, immune cell recruitment, and immune evasion. Therefore, understanding the intricate interplay between HAIMs and HA within the cancer context is essential for developing novel therapeutic strategies. Targeting these interactions offers promising avenues for cancer treatment, as they hold the potential to disrupt critical aspects of disease progression and the TME. Further research in this field is imperative for advancing our knowledge and the treatment of cancer.

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.

Mechanism insights of curcumin and its analogues in cancer: An update

Cancer is the world's second leading cause of mortality and one of the major public health problems. Cancer incidence and mortality rates remain high despite the great advancements in existing therapeutic, diagnostic, and preventive approaches. Therefore, a quest for less toxic and more efficient anti-cancer strategies is still at the forefront of the current research. Traditionally important, curcumin commonly known as a wonder molecule has received considerable attention as an anti-cancer, anti-inflammatory, and antioxidant candidate. However, limited water solubility and low bioavailability restrict its extensive utility in different pathological states. The investigators are making consistent efforts to develop newer strategies to overcome its limitations by designing different analogues with better pharmacokinetic and pharmacodynamic properties. The present review highlights the recent updates on curcumin and its analogues with special emphasis on various mechanistic pathways involved in anti-cancer activity. In addition, the structure-activity relationship of curcumin analogues has also been precisely discussed. This article will also provide key information for the design and development of newer curcumin analogues with desired pharmacokinetic and pharmacodynamic profiles and will provide in depth understanding of molecular pathways involved in the anti-cancer activities.

Combination of DNA methylation biomarkers with multiparametric magnetic resonance and ultrasound imaging fusion biopsy to detect the local spread of prostate cancer

BACKGROUND

This study aimed to investigate the extent of field cancerization adjacent to index lesions in prostate cancer (PCa) by measuring DNA methylation of selected tumor suppressor genes in the perifocal tissue of PCa not visible on multiparametric magnetic resonanse imaging (mpMRI) for the safe zone of focal therapy identification.

METHODS

A total of 272 patients were enrolled in this study, 44 patients' tissue biosamples were included in the field cancerization research, and 272 urine samples were included in the urine-based test development. Targeted biopsies were performed using the mpMRI/ultrasoundimage fusion system.

RESULTS

Quantitative analysis revealed significantly higher DNA methylation levels of RARB, RASSF1, GSTP1 & APC genes in the index lesion compared with perifocal tissue samples 10 mm away from it (p < 0.0001). Notably, the RARB, GSTP1 & APC and RARB, RASSF1, GSTP1 & APC biomarker combinations exhibited the highest sensitivity and specificity comparing the extent of DNA methylation in index lesions and noncancerous prostate tissues 20 mm away (both area under the curve [AUC] = 0.98; p < 0.0001). The analysis of the potential urinary biomarkers showed that the combination of all four DNA methylation biomarkers with prostate-specific antigen (PSA) or PSA density (PSAD) in the blood significantly improves the detection of clinically significant PCa (csPCa). The combination of the four-biomarker test with PSAD allowed the identification of csPCa with ≥90% sensitivity and specificity.

CONCLUSION

Thus, this study suggests that for focal therapy by region target hemi-ablation, the safe distance from the index lesion is no less than 10 mm. Noninvasive urine DNA methylation tests in combination with PSAD could be used for further follow-up of the patients, but larger prospective studies with external validation are needed.

A view of the genetic and proteomic profile of extracellular matrix molecules in aging and stroke

Introduction

Modification of the extracellular matrix (ECM) is one of the major processes in the pathology of brain damage following an ischemic stroke. However, our understanding of how age-related ECM alterations may affect stroke pathophysiology and its outcome is still very limited.

Methods

We conducted an ECM-targeted re-analysis of our previously obtained RNA-Seq dataset of aging, ischemic stroke and their interactions in young adult (3-month-old) and aged (18-month-old) mice. The permanent middle cerebral artery occlusion (pMCAo) in rodents was used as a model of ischemic stroke. Altogether 56 genes of interest were chosen for this study.

Results

We identified an increased activation of the genes encoding proteins related to ECM degradation, such as matrix metalloproteinases (MMPs), proteases of a disintegrin and metalloproteinase with the thrombospondin motifs (ADAMTS) family and molecules that regulate their activity, tissue inhibitors of metalloproteinases (TIMPs). Moreover, significant upregulation was also detected in the mRNA of other ECM molecules, such as proteoglycans, syndecans and link proteins. Notably, we identified 8 genes where this upregulation was enhanced in aged mice in comparison with the young ones. Ischemia evoked a significant downregulation in only 6 of our genes of interest, including those encoding proteins associated with the protective function of ECM molecules (e.g., brevican, Hapln4, Sparcl1); downregulation in brevican was more prominent in aged mice. The study was expanded by proteome analysis, where we observed an ischemia-induced overexpression in three proteins, which are associated with neuroinflammation (fibronectin and vitronectin) and neurodegeneration (link protein Hapln2). In fibronectin and Hapln2, this overexpression was more pronounced in aged post-ischemic animals.

Conclusion

Based on these results, we can conclude that the ratio between the protecting and degrading mechanisms in the aged brain is shifted toward degradation and contributes to the aged tissues' increased sensitivity to ischemic insults. Altogether, our data provide fresh perspectives on the processes underlying ischemic injury in the aging brain and serve as a freely accessible resource for upcoming research.

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.

Screening potential biomarkers of cholangiocarcinoma based on gene chip meta-analysis and small-sample experimental research

Cholangiocarcinoma (CCA) is a rare malignant tumor associated with poor prognosis. This study aimed to identify CCA biomarkers by investigating differentially expressed genes (DEGs) between CCA patients and healthy subjects obtained from the Gene Expression Omnibus database. Bioinformatics tools, including the Illumina BaseSpace Correlation Engine (BSCE) and Gene Expression Profiling Interactive Analysis (GEPIA), were used. The initial DEGs from GSE26566, GSE31370, and GSE77984 were analyzed using GEO2R and Venn, and protein–protein interaction networks were constructed using STRING. The BSCE was applied to assess curated CCA studies to select additional DEGs and them DEGs across the 10 biosets, which was supported by findings in the literature. The final 18 DEGs with clinical significance for CCA were further verified using GEPIA. These included CEACAM6, EPCAM, LAMC2, MMP11, KRT7, KRT17, KRT19, SFN, and SOX9, which were upregulated, and ADH1A, ALDOB, AOX1, CTH, FGA, FGB, FGG, GSTA1, and OTC, which were downregulated in CCA patients. Among these 18 genes, 56 groups of genes (two in each group) were significantly related, and none were independently and differentially expressed. The hub genes FGA, OTC, CTH, and MMP11, which were most correlated with the 18 DEGs, were screened using STRING. The significantly low expression of FGA, OTC, and CTH and significantly high expression of MMP11 were verified by immunohistochemical analysis. Overall, four CCA biomarkers were identified that might regulate the occurrence and development of this disease and affect the patient survival rate, and they have the potential to become diagnostic and therapeutic targets for patients with CCA.

Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis

Developing cancer prognostic models using multiomics data is a major goal of precision oncology. DNA methylation provides promising prognostic biomarkers, which have been used to predict survival and treatment response in solid tumor or plasma samples. This review article presents an overview of recently published computational analyses on DNA methylation for cancer prognosis. To address the challenges of survival analysis with high-dimensional methylation data, various feature selection methods have been applied to screen a subset of informative markers. Using candidate markers associated with survival, prognostic models either predict risk scores or stratify patients into subtypes. The model's discriminatory power can be assessed by multiple evaluation metrics. Finally, we discuss the limitations of existing studies and present the prospects of applying machine learning algorithms to fully exploit the prognostic value of DNA methylation.

DNA Methylation-Mediated Lowly Expressed AOX1 Promotes Cell Migration and Invasion of Prostate Cancer

INTRODUCTION

DNA methylation regulates gene transcriptional functions in the pathogenesis of malignant diseases. In prostate cancer, several tumor suppressors are known to be tumor specifically methylated.

METHODS

In this study, 450K methylation data and mRNA expression data were accessed from The Cancer Genome Atlas-Prostate Adenocarcinoma database and analyzed bioinformatically. Methylation-specific PCR was used to examine the methylation condition in AOX1 promoter. qRT-PCR was applied to measure the mRNA expression of AOX1. Western blot was employed to detect the expressions of AOX1 and the EMT associated proteins. Transwell and scratch healing assays were used to examine the invasive and migratory abilities of the prostate cancer cells respectively.

RESULTS

AOX1 was lowly expressed and hypermethylated in the prostate cancer tissues and cells. Also, AOX1 was downregulated at protein level in prostate cancer cells. Knocking down AOX1 could promote cell migration and invasion in the prostate cancer cells. By using a DNA methylation inhibitor, 5-AzadC was found to promote the expression of AOX1 and reverse the promoting effects of short interfering RNA against AOX1 on cell migration and invasion.

CONCLUSION

This study suggested that DNA methylation and low AOX1 level might be biomarkers for prostate cancer.

SEEMLIS: a flexible semi-automated method for enrichment of methylated DNA from low-input samples

Background

DNA methylation alterations have emerged as hallmarks of cancer and have been proposed as screening, prognostic, and predictive biomarkers. Traditional approaches for methylation analysis have relied on bisulfite conversion of DNA, which can damage DNA and is not suitable for targeted gene analysis in low-input samples. Here, we have adapted methyl-CpG-binding domain protein 2 (MBD2)-based DNA enrichment for use on a semi-automated exclusion-based sample preparation (ESP) platform for robust and scalable enrichment of methylated DNA from low-input samples, called SEEMLIS.

Results

We show that combining methylation-sensitive enzyme digestion with ESP-based MBD2 enrichment allows for single gene analysis with high sensitivity for GSTP1 in highly impure, heterogenous samples. We also show that ESP-based MBD2 enrichment coupled with targeted pre-amplification allows for analysis of multiple genes with sensitivities approaching the single cell level in pure samples for GSTP1 and RASSF1 and sensitivity down to 14 cells for these genes in highly impure samples. Finally, we demonstrate the potential clinical utility of SEEMLIS by successful detection of methylated gene signatures in circulating tumor cells (CTCs) from patients with prostate cancer with varying CTC number and sample purity.

Conclusions

SEEMLIS is a robust assay for targeted DNA methylation analysis in low-input samples, with flexibility at multiple steps. We demonstrate the feasibility of this assay to analyze DNA methylation in prostate cancer cells using CTCs from patients with prostate cancer as a real-world example of a low-input analyte of clinical importance. In summary, this novel assay provides a platform for determining methylation signatures in rare cell populations with broad implications for research as well as clinical applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01252-4.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature’s applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

Histologically benign PI-RADS 4 and 5 lesions contain cancer-associated epigenetic alterations

BACKGROUND

The detection rate of clinically significant prostate cancer has improved with the use of multiparametric magnetic resonance imaging (mpMRI). Yet, even with MRI-guided biopsy 15%-35% of high-risk lesions (Prostate Imaging-Reporting and Data System [PI-RADS] 4 and 5) are histologically benign. It is unclear if these false positives are due to diagnostic/sampling errors or pathophysiological alterations. To better understand this, we tested histologically benign PI-RAD 4 and 5 lesions for common malignant epigenetic alterations.

MATERIALS AND METHODS

MRI-guided in-bore biopsy samples were collected from 45 patients with PI-RADS 4 (n = 31) or 5 (n = 14) lesions. Patients had a median clinical follow-up of 3.8 years. High-risk mpMRI patients were grouped based on their histology into biopsy positive for tumor (BPT; n = 28) or biopsy negative for tumor (BNT; n = 17). From these biopsy samples, DNA methylation of well-known tumor suppressor genes (APC, GSTP1, and RARβ2) was quantified.

RESULTS

Similar to previous work we observed high rates of promoter methylation at GSTP1 (92.7%), RARβ2 (57.3%), and APC (37.8%) in malignant BPT samples but no methylation in benign TURP chips. Interestingly, similar to the malignant samples the BNT biopsies also had increased methylation at the promoter of GSTP1 (78.8%) and RARβ2 (34.6%). However, despite these epigenetic alterations none of these BNT patients developed prostate cancer, and those who underwent repeat mpMRI (n = 8) demonstrated either radiological regression or stability.

CONCLUSIONS

Histologically benign PI-RADS 4 and 5 lesions harbor prostate cancer-associated epigenetic alterations.

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.

Bridging the Gaps between Circulating Tumor Cells and DNA Methylation in Prostate Cancer

Prostate cancer is the second most common male malignancy, with a highly variable clinical presentation and outcome. Therefore, diagnosis, prognostication, and management remain a challenge, as available clinical, imaging, and pathological parameters provide limited risk assessment. Thus, many biomarkers are under study to fill this critical gap, some of them based on epigenetic aberrations that might be detected in liquid biopsies. Herein, we provide a critical review of published data on the usefulness of DNA methylation and circulating tumor cells in diagnosis and treatment decisions in cases of prostate cancer, underlining key aspects and discussing the importance of these advances to the improvement of the management of prostate cancer patients. Using minimally invasive blood tests, the detection of highly specific biomarkers might be crucial for making therapeutic decisions, determining response to specific treatments, and allowing early diagnosis.

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.

Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.

Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.

Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an excellent prognostic efficiency for 1, 3, and 5-year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.

Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.

Subjects: Bioinformatics, oncology, urology.

Precision Medicine Approach in Prostate Cancer

Prostate cancer is the most prevalent type of cancer and the second cause of death in men worldwide. Various diagnostic and treatment procedures are available for this type of malignancy, but High-grade or locally advanced prostate cancers showed the potential to develop to lethal phase that can be causing dead. Therefore, new approaches are needed to prolong patients' survival and to improve their quality of life. Precision medicine is a novel emerging field that plays an essential role in identifying new sub-classifications of diseases and in providing guidance in treatment that is based on individual multi-omics data. Multi-omics approaches include the use of genomics, transcriptomics, proteomics, metabolomics, epigenomics and phenomics data to unravel the complexity of a disease-associated biological network, to predict prognostic biomarkers, and to identify new targeted drugs for individual cancer patients. We review the impact of multi-omics data in the framework of systems biology in the era of precision medicine, emphasising the combination of molecular imaging modalities with highthroughput techniques and the new treatments that target metabolic pathways involved in prostate cancer.

Differential DNA Methylation in Prostate Tumors from Puerto Rican Men

In 2020, approximately 191,930 new prostate cancer (PCa) cases are estimated in the United States (US). Hispanic/Latinos (H/L) are the second largest racial/ethnic group in the US. This study aims to assess methylation patterns between aggressive and indolent PCa including DNA repair genes along with ancestry proportions. Prostate tumors classified as aggressive (n = 11) and indolent (n = 13) on the basis of the Gleason score were collected. Tumor and adjacent normal tissue were annotated on H&E (Haemotoxylin and Eosin) slides and extracted by macro-dissection. Methylation patterns were assessed using the Illumina 850K DNA methylation platform. Raw data were processed using the Bioconductor package. Global ancestry proportions were estimated using ADMIXTURE (k = 3). One hundred eight genes including AOX1 were differentially methylated in tumor samples. Regarding the PCa aggressiveness, six hypermethylated genes (RREB1, FAM71F2, JMJD1C, COL5A3, RAE1, and GABRQ) and 11 hypomethylated genes (COL9A2, FAM179A, SLC17A2, PDE10A, PLEKHS1, TNNI2, OR51A4, RNF169, SPNS2, ADAMTSL5, and CYP4F12) were identified. Two significant differentially methylated DNA repair genes, JMJD1C and RNF169, were found. Ancestry proportion results for African, European, and Indigenous American were 24.1%, 64.2%, and 11.7%, respectively. The identification of DNA methylation patterns related to PCa in H/L men along with specific patterns related to aggressiveness and DNA repair constitutes a pivotal effort for the understanding of PCa in this population.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

Methylation of immune-regulatory cytokine genes and pancreatic cancer outcomes

Aim: Given the immunosuppressive nature of pancreatic cancer, we investigated the relationship between epigenetic modification of immune-regulatory cytokine genes and pancreatic cancer outcomes. Materials & methods: We evaluated DNA methylation of 184 pancreatic tumor samples from The Cancer Genome Atlas for 111 CpG loci in seven cytokine genes: IL10, IL6, IL8, TGFβ1, TGFβ2, TGFβ3 and TNF. We used Cox regression to evaluate the associations between methylation and overall survival, disease-specific survival and disease progression (α = 0.05). Results: Poorer survival was associated with increased methylation in fifteen CpG probes in TGFβ1, TGFβ2, TGFβ3 and TNF. We also detected improved outcomes for three loci in IL10, IL8 and IL6. Conclusion: Epigenetic regulation of cytokine-related gene expression may be associated with pancreatic cancer outcomes.

Epigenetic Analysis of Circulating Tumor DNA in Localized and Metastatic Prostate Cancer: Evaluation of Clinical Biomarker Potential

Novel and minimally-invasive prostate cancer (PCa)-specific biomarkers are needed to improve diagnosis and risk stratification. Here, we investigated the biomarker potential in localized and de novo metastatic PCa (mPCa) of methylated circulating tumor DNA (ctDNA) in plasma. Using the Marmal-aid database and in-house datasets, we identified three top candidates specifically hypermethylated in PCa tissue: DOCK2, HAPLN3, and FBXO30 (specificity/sensitivity: 80%-100%/75-94%). These candidates were further analyzed in plasma samples from 36 healthy controls, 61 benign prostatic hyperplasia (BPH), 102 localized PCa, and 65 de novo mPCa patients using methylation-specific droplet digital PCR. Methylated ctDNA for DOCK2/HAPLN3/FBXO30 was generally not detected in healthy controls, BPH patients, nor in patients with localized PCa despite a positive signal in 98%-100% of matched radical prostatectomy tissue samples. However, ctDNA methylation of DOCK2, HAPLN3, and/or FBXO30 was detected in 61.5% (40/65) of de novo mPCa patients and markedly increased in high- compared to low-volume mPCa (89.3% (25/28) vs. 32.1% (10/31), p < 0.001). Moreover, detection of methylated ctDNA was associated with significantly shorter time to progression to metastatic castration resistant PCa, independent of tumor-volume. These results indicate that methylated ctDNA (DOCK2/HAPLN3/FBXO30) may be potentially useful for identification of hormone-naïve mPCa patients who could benefit from intensified treatment.

Dataset of GWAS-identified variants underlying venous thromboembolism susceptibility and linkage to cancer aggressiveness

Venous thromboembolism (VTE) is a common cardiovascular disease, for which several single nucleotide polymorphisms (SNPs) underlying susceptibility were identified. Apart from candidate gene approach, genome-wide association studies (GWAS) have contributed to the identification of novel VTE-associated SNPs, including some with no clear role in the haemostatic system. These genetic variants constitute potential cancer-related biomarkers, particularly predictive and prognostic biomarkers, as a two-way association between VTE and cancer is well established. The present dataset comprises the data obtained from GWAS performed to identify genetic variants associated with VTE risk. Furthermore, this dataset also comprises data regarding previously reported candidate gene and validation reports performed in adults of European ancestry that also analysed the VTE GWAS-identified variants. Lastly, to evaluate the impact of these genetic variants in carcinogenesis, a broad search was made, which has let us to establish putative links between several VTE-associated genes and cancer hallmarks in a review article entitled “Venous thromboembolism GWAS reported genetic makeup and the hallmarks of cancer: linkage to ovarian tumour behaviour”.

EBADIMEX: an empirical Bayes approach to detect joint differential expression and methylation and to classify samples

DNA methylation and gene expression are interdependent and both implicated in cancer development and progression, with many individual biomarkers discovered. A joint analysis of the two data types can potentially lead to biological insights that are not discoverable with separate analyses. To optimally leverage the joint data for identifying perturbed genes and classifying clinical cancer samples, it is important to accurately model the interactions between the two data types. Here, we present EBADIMEX for jointly identifying differential expression and methylation and classifying samples. The moderated t-test widely used with empirical Bayes priors in current differential expression methods is generalised to a multivariate setting by developing: (1) a moderated Welch t-test for equality of means with unequal variances; (2) a moderated F-test for equality of variances; and (3) a multivariate test for equality of means with equal variances. This leads to parametric models with prior distributions for the parameters, which allow fast evaluation and robust analysis of small data sets. EBADIMEX is demonstrated on simulated data as well as a large breast cancer (BRCA) cohort from TCGA. We show that the use of empirical Bayes priors and moderated tests works particularly well on small data sets.

DNA methylation in repeat negative prostate biopsies as a marker of missed prostate cancer

Background

Men often undergo repeat prostate biopsies because of suspicion of missed cancer. We assessed if (i) methylation of selected genes in prostate tissue vary with aging and (ii) methylation alterations in repeat biopsies predict missed prostate cancer.

Methods

We conducted a case-control study among men who underwent at least two negative prostate biopsies followed by a sampling either positive (cases n = 111) or negative (controls n = 129) for prostate cancer between 1995 and 2014 at the University Hospital (Turin, Italy). Two pathology wards were included for replication purposes. We analyzed methylation of GSTP1, APC, PITX2, C1orf114, GABRE, and LINE-1 in the first two negative biopsies. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of the association between genes methylation and prostate cancer.

Results

Age at biopsy and time interval between the two negative biopsies were not associated with methylation levels of the selected genes in neither cases nor controls. GSTP1 methylation in the first and in the second negative biopsy was associated with prostate cancer detection [OR per 1% increase: 1.14 (95% CI 1.01–1.29) for the second biopsy and 1.21 (95% CI 1.07–1.37) for the highest methylation level (first or second biopsy)]. A threshold > 10% for GSTP1 methylation corresponded to a specificity of 0.98 (positive likelihood ratio 7.87). No clear association was found for the other genes. Results were consistent between wards.

Conclusions

Our results suggest that GSTP1 methylation in negative prostate biopsies is stable over time and can predict missed cancer with high specificity.

Epigenetic silencing of MEIS2 in prostate cancer recurrence

Background

Current diagnostic and prognostic tools for prostate cancer (PC) are suboptimal, resulting in overdiagnosis and overtreatment of clinically insignificant tumors. Thus, to improve the management of PC, novel biomarkers are urgently needed.

Results

In this study, we integrated genome-wide methylome (Illumina 450K DNA methylation array (450K)) and RNA sequencing (RNAseq) data performed in a discovery set of 27 PC and 15 adjacent normal (AN) prostate tissue samples to identify candidate driver genes involved in PC development and/or progression. We found significant enrichment for homeobox genes among the most aberrantly methylated and transcriptionally dysregulated genes in PC. Specifically, homeobox gene MEIS2 (Myeloid Ecotropic viral Insertion Site 2) was significantly hypermethylated (p < 0.0001, Mann-Whitney test) and transcriptionally downregulated (p < 0.0001, Mann-Whitney test) in PC compared to non-malignant prostate tissue in our discovery sample set, which was also confirmed in an independent validation set including > 500 PC and AN tissue samples in total (TCGA cohort analyzed by 450K and RNAseq). Furthermore, in three independent radical prostatectomy (RP) cohorts (n > 700 patients in total), low MEIS2 transcriptional expression was significantly associated with poor biochemical recurrence (BCR) free survival (p = 0.0084, 0.0001, and 0.0191, respectively; log-rank test). Next, we analyzed another RP cohort consisting of > 200 PC, AN, and benign prostatic hyperplasia (BPH) samples by quantitative methylation-specific PCR (qMSP) and found that MEIS2 was significantly hypermethylated (p < 0.0001, Mann-Whitney test) in PC compared to non-malignant prostate tissue samples (AN and BPH) with an AUC > 0.84. Moreover, in this cohort, aberrant MEIS2 hypermethylation was significantly associated with post-operative BCR (p = 0.0068, log-rank test), which was subsequently confirmed (p = 0.0067; log-rank test) in the independent TCGA validation cohort (497 RP patients; 450K data).

Conclusions

To the best of our knowledge, this is the first study to investigate, demonstrate, and independently validate a prognostic biomarker potential for MEIS2 at the transcriptional expression level and at the DNA methylation level in PC.

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

BACKGROUND

We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations.

MATERIALS AND METHODS

We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated.

RESULTS

In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation.

CONCLUSION

We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Identification of key DNA methylation-driven genes in prostate adenocarcinoma: an integrative analysis of TCGA methylation data

Background

Prostate cancer (PCa) remains the second leading cause of deaths due to cancer in the United States in men. The aim of this study was to perform an integrative epigenetic analysis of prostate adenocarcinoma to explore the epigenetic abnormalities involved in the development and progression of prostate adenocarcinoma. The key DNA methylation-driven genes were also identified.

Methods

Methylation and RNA-seq data were downloaded for The Cancer Genome Atlas (TCGA). Methylation and gene expression data from TCGA were incorporated and analyzed using MethylMix package. Methylation data from the Gene Expression Omnibus (GEO) were assessed by R package limma to obtain differentially methylated genes. Pathway analysis was performed on genes identified by MethylMix criteria using ConsensusPathDB. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also applied for the identification of pathways in which DNA methylation-driven genes significantly enriched. The protein–protein interaction (PPI) network and module analysis in Cytoscape software were used to find the hub genes. Two methylation profile (GSE112047 and GSE76938) datasets were utilized to validate screened hub genes. Immunohistochemistry of these hub genes were evaluated by the Human Protein Atlas.

Results

A total of 553 samples in TCGA database, 32 samples in GSE112047 and 136 samples in GSE76938 were included in this study. There were a total of 266 differentially methylated genes were identified by MethylMix. Plus, a total of 369 differentially methylated genes and 594 differentially methylated genes were identified by the R package limma in GSE112047 and GSE76938, respectively. GO term enrichment analysis suggested that DNA methylation-driven genes significantly enriched in oxidation–reduction process, extracellular exosome, electron carrier activity, response to reactive oxygen species, and aldehyde dehydrogenase [NAD(P)+] activity. KEGG pathway analysis found DNA methylation-driven genes significantly enriched in five pathways including drug metabolism—cytochrome P450, phenylalanine metabolism, histidine metabolism, glutathione metabolism, and tyrosine metabolism. The validated hub genes were MAOB and RTP4.

Conclusions

Methylated hub genes, including MAOB and RTP4, can be regarded as novel biomarkers for accurate PCa diagnosis and treatment. Further studies are needed to draw more attention to the roles of these hub genes in the occurrence and development of PCa.

SLCO4C1 promoter methylation is a potential biomarker for prognosis associated with biochemical recurrence-free survival after radical prostatectomy

BACKGROUND

Prostate cancer (PC) is a commonly diagnosed malignancy in males, especially in the western hemisphere. The extensive use of multiple biomarkers plays an important role in the diagnosis and prognosis of PC. However, the accuracy of biomarkers for PC prognosis needs to be urgently improved. This study aimed to identify a novel prognostic biomarker for PC.

MATERIALS AND METHODS

Differentially methylated CpG sites were identified from the GSE76938 dataset ( https://www.ncbi.nlm.nih.gov/geo/ ) using R software version 3.1.4. Four significant CpG sites on the SLCO4C1 gene were found to be closely associated with prognosis in PC. Data downloaded from The Cancer Genome Atlas (TCGA) were used for validation. Co-expression and functional enrichment analyses were used to explore the roles of SLCO4C1 in molecular functions, biological processes and cellular components. Total RNA extraction and qRT-PCR were used to reveal the difference in SLCO4C1 expression between tumour and normal tissues. Bisulfite amplicon sequencing (BSAS) was used to identify methylation levels at the CpG sites.

RESULTS

In the GSE76938 cohort, 10,206 CpG sites were identified to be differentially methylated in tumour versus normal prostate tissues. Among the CpG sites, four sites (cg06480736, cg19774478, cg19788741 and cg22149516) located in the promotor region (TSS200-1500) of SLCO4C1 were found to be significantly hypermethylated in tumour tissues. The results were validated in an independent dataset (TCGA PRAD cohort). In the cohort from TCGA, SLCO4C1 expression was negatively correlated with methylation levels at the four sites. The results of qRT-PCR validated that tumour tissues had a relatively lower expression of SLCO4C1. Bisulfite amplicon sequencing (BSAS) further confirmed a higher methylation level at the SLCO4C1 promoter in tumour tissues. SLCO4C1 (cg06480736, cg19774478, cg19788741 and cg22149516) was identified as a significant promising biomarker for biochemical recurrence-free survival in Kaplan-Meier analysis (P < 0.01) and univariate Cox proportional hazards analysis: cg06480736 (HR 15.914, P < 0.001), cg19774478 (HR 9.001, P < 0.001), cg19788741 (HR 10.759, P = 0.003) and cg22149516 (HR 17.144, P = 0.006). However, three sites, namely, cg06480736 (HR 1.809, P = 0.049), cg19774478 (HR 1.903, P = 0.041) and cg22149516 (HR 2.316, P = 0.008), were confirmed in multivariate analysis.

CONCLUSIONS

SLCO4C1 promoter methylation, including that at three CpG sites, namely, cg06480736, cg19774478 and cg22149516, is a potential biomarker for risk stratification and might offer significantly relevant prognostic information for PC patients after radical prostatectomy.

Quantitative Methylation-Specific PCR: A Simple Method for Studying Epigenetic Modifications of Cell-Free DNA

Aberrant DNA methylation of cell-free circulating DNA (cfDNA) has recently gained attention for its use as biomarker in cancer diagnosis, prognosis, and prediction of therapeutic response. Quantification of cfDNA methylation levels requires methods with high sensitivity and specificity due to low amounts of cfDNA available in plasma, high degradation of cfDNA, and/or contamination with genomic DNA. To date, several approaches for measuring cfDNA methylation have been established, including quantitative methylation-specific PCR (qMSP), which represents a simple, fast, and cost-effective technique that can be easily implemented into clinical practice. In this chapter, we provide a detailed protocol for SYBR Green qMSP analysis which is currently used in our laboratory for cfDNA methylation detection. Useful information regarding successful qMSP primers design are also provided.

Sp1 contributes to overexpression of stanniocalcin 2 through regulation of promoter activity in colon adenocarcinoma

BACKGROUND

Aberrant expression of stanniocalcin 2 (STC2) is implicated in colon adenocarcinoma (COAD). A previous study identified that STC2 functions as a tumor promoter to drive development of some cancers, but the role of its overexpression in the development of COAD remains unclear.

AIM

To evaluate the regulation mechanism of STC2 overexpression in COAD.

METHODS

The expression of STC2 in COAD was assessed by TCGA COAD database and GEO (GSE50760). Methylation level of the STC2 promoter was evaluated with beta value in UALCAN platform, and the correlation between STC2 expression and survival rate was investigated with TCGA COAD. Transcription binding site prediction was conducted by TRANSFAC and LASAGNA, and a luciferase reporter system was used to identify STC2 promoter activity in several cell lines, including HEK293T, NCM460, HT29, SW480, and HCT116. Western blotting was performed to evaluate the role of Sp1 on the expression of STC2.

RESULTS

The central finding of this work is that STC2 is overexpressed in COAD tissues and positively correlated with poor prognosis. Importantly, the binding site of the transcription factor Sp1 is widely located in the promoter region of STC2. A luciferase reporter system was successfully constructed to analyze the transcription activity of STC2, and knocking down the expression of Sp1 significantly inhibited the transcription activity of STC2. Furthermore, inhibition of Sp1 remarkably decreased protein levels of STC2.

CONCLUSION

Our data provide evidence that the transcription factor Sp1 is essential for the overexpression of STC2 in COAD through activation of promoter activity. Taken together, our finding provides new insights into the mechanism of oncogenic function of COAD by STC2.

A novel combined miRNA and methylation marker panel (miMe) for prediction of prostate cancer outcome after radical prostatectomy

Improved prognostic biomarkers are needed to guide personalized prostate cancer (PC) treatment decisions. Due to the prominent molecular heterogeneity of PC, multimarker panels may be more robust. Here, 25 selected top-candidate miRNA and methylation markers for PC were profiled by qPCR in malignant radical prostatectomy (RP) tissue specimens from 198 PC patients (Cohort 1, training). Using GLMnet, we trained a novel multimarker model (miMe) comprising nine miRNAs and three methylation markers that predicted postoperative biochemical recurrence (BCR) independently of the established clinicopathological CAPRA-S nomogram in Cox multivariate regression analysis in Cohort 1 (HR [95% CI]: 1.53 [1.26-1.84], p < 0.001). This result was successfully validated in two independent RP cohorts (Cohort 2, n = 159: HR [95% CI]: 1.35 [1.06-1.73], p = 0.015. TCGA, n = 350: HR [95% CI]: 1.34 [1.01-1.77], p = 0.04). Notably, in CAPRA-S low-risk patients, a high miMe score was associated with >6 times higher risk of BCR, suggesting that miMe may help identify PC patients at high risk of progression despite favorable clinicopathological factors postsurgery. Finally, miMe was a significant predictor of cancer-specific survival (p = 0.019, log-rank test) in a merged analysis of 357 RP patients. In conclusion, we trained, tested and validated a novel 12-marker panel (miMe) that showed significant independent prognostic value in three RP cohorts. In the future, combining miMe score with existing clinical nomograms may improve PC risk stratification and thus help guide treatment decisions.

Differential gene methylation patterns in cancerous and non‑cancerous cells

Large-scale projects, such as The Cancer Genome Atlas (TCGA), Human Epigenome Project (HEP) and Human Epigenome Atlas (HEA), provide an insight into DNA methylation and histone modification markers. Changes in the epigenome significantly contribute to the initiation and progression of cancer. The goal of the present study was to characterize the prostate cancer malignant transformation model using the CpG island methylation pattern. The Human Prostate Cancer EpiTect Methyl II Signature PCR Array was used to evaluate the methylation status of 22 genes in prostate cancer cell lines: PC3, PC3M, PC3MPro4 and PC3MLN4, each representing different metastatic potential in vivo. Subsequently, it was ascertained whether DNA methylation plays a role in the expression of these genes in prostate cancer cells. Hypermethylation of APC, DKK3, GPX3, GSTP1, MGMT, PTGS2, RASSF1, TIMP2 and TNFRSF10D resulted in downregulation of their expression in prostate cancer cell lines as compared to WT fibroblasts. Mining of the TCGA data deposited in the MetHC database found increases in the methylation status of these 9 genes in prostate cancer patients, further supporting the role of methylation in altering the expression of these genes in prostate cancer. Future studies are warranted to investigate the role of these proteins in prostate cancer development.

Mutational landscape of penile squamous cell carcinoma in a Chinese population

Penile squamous cell carcinoma (PSCC) is a malignancy that affects the skin and tissues of the penis, but the knowledge of pathogenesis and carcinogenesis is limited. Here, we characterize the PSCC genomic landscape using whole-exome sequencing. Of the 30 paired blood and tumor samples, we identified recurrent mutations in 11 genes; confirmed previous findings for FAT1 (4/30), HRAS (4/30), NOTCH1 (4/30), TP53 (3/30) and PIK3CA (3/30); and revealed novel candidate driver genes [CASP8 (4/30), SLITRK2 (3/30), FLG (3/30) and TRRAP (3/30)]. Our in vitro experiments suggested CASP8 was involved in mediating TRAIL-induced apoptosis of penile cancer cell lines. We also observed the frequently altered pathways for potential therapeutic implications: alterations in the Notch (30% of sample altered), RTK-RAS (26.7% altered) and Hippo (23.3% altered) pathways accounted for over 50% of tumors. The frequently altered genes (>10%) in these pathways were proved to be expressed in penile tumors by immunohistochemistry assay. These findings provide new insight into the mutational and pathway landscapes of PSCC and suggest potential novel therapeutic opportunities for this malignancy.

Aberrant DOCK2, GRASP, HIF3A and PKFP Hypermethylation has Potential as a Prognostic Biomarker for Prostate Cancer

Prostate cancer (PCa) is a clinically heterogeneous disease and currently, accurate diagnostic and prognostic molecular biomarkers are lacking. This study aimed to identify novel DNA hypermethylation markers for PCa with future potential for blood-based testing. Accordingly, to search for genes specifically hypermethylated in PCa tissue samples and not in blood cells or other cancer tissue types, we performed a systematic analysis of genome-wide DNA methylation data (Infinium 450K array) available in the Marmal-aid database for 4072 malignant/normal tissue samples of various types. We identified eight top candidate markers (cg12799885, DOCK2, FBXO30, GRASP, HIF3A, MOB3B, PFKP, and TPM4) that were specifically hypermethylated in PCa tissue samples and hypomethylated in other benign and malignant tissue types, including in peripheral blood cells. Potential as diagnostic and prognostic biomarkers was further assessed by the quantitative methylation specific PCR (qMSP) analysis of 37 nonmalignant and 197 PCa tissue samples from an independent population. Here, all eight hypermethylated candidates showed high sensitivity (75–94%) and specificity (84–100%) for PCa. Furthermore, DOCK2, GRASP, HIF3A and PKFP hypermethylation was significantly associated with biochemical recurrence (BCR) after radical prostatectomy (RP; 197 patients), independent of the routine clinicopathological variables. DOCK2 is the most promising single candidate marker (hazard ratio (HR) (95% confidence interval (CI)): 1.96 (1.24–3.10), adjusted p = 0.016; multivariate cox regression). Further validation studies are warranted and should investigate the potential value of these hypermethylation candidate markers for blood-based testing also.

Phenotype-independent DNA methylation changes in prostate cancer

Background

Human prostate cancers display numerous DNA methylation changes compared to normal tissue samples. However, definitive identification of features related to the cells’ malignant status has been compromised by the predominance of cells with luminal features in prostate cancers.

Methods

We generated genome-wide DNA methylation profiles of cell subpopulations with basal or luminal features isolated from matched prostate cancer and normal tissue samples.

Results

Many frequent DNA methylation changes previously attributed to prostate cancers are here identified as differences between luminal and basal cells in both normal and cancer samples. We also identified changes unique to each of the two cancer subpopulations. Those specific to cancer luminal cells were associated with regulation of metabolic processes, cell proliferation and epithelial development. Within the prostate cancer TCGA dataset, these changes were able to distinguish not only cancers from normal samples, but also organ-confined cancers from those with extraprostatic extensions. Using changes present in both basal and luminal cancer cells, we derived a new 17-CpG prostate cancer signature with high predictive power in the TCGA dataset.

Conclusions

This study demonstrates the importance of comparing phenotypically matched prostate cell populations from normal and cancer tissues to unmask biologically and clinically relevant DNA methylation changes.

Genome-Based Classification and Therapy of Prostate Cancer

In the past decade, multi-national and multi-center efforts were launched to sequence prostate cancer genomes, transcriptomes, and epigenomes with the aim of discovering the molecular underpinnings of tumorigenesis, cancer progression, and therapy resistance. Multiple biological markers and pathways have been discovered to be tumor drivers, and a molecular classification of prostate cancer is emerging. Here, we highlight crucial findings of these genome-sequencing projects in localized and advanced disease. We recapitulate the utility and limitations of current clinical practices to diagnosis, prognosis, and therapy, and we provide examples of insights generated by the molecular profiling of tumors. Novel treatment concepts based on these molecular alterations are currently being addressed in clinical trials and will lead to an enhanced implementation of precision medicine strategies.

Dysregulation and prognostic potential of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) levels in prostate cancer

Background

Prognostic tools for prostate cancer (PC) are inadequate and new molecular biomarkers may improve risk stratification. The epigenetic mark 5-hydroxymethylcytosine (5hmC) has recently been proposed as a novel candidate prognostic biomarker in several malignancies including PC. 5hmC is an oxidized derivative of 5-methylcytosine (5mC) and can be further oxidized to 5-formylcytosine (5fC) and 5-carboxylcytosine (5caC). The present study is the first to investigate the biomarker potential in PC for all four DNA methylation marks in parallel. Thus, we determined 5mC, 5hmC, 5fC, and 5caC levels in non-malignant (NM) and PC tissue samples from a large radical prostatectomy (RP) patient cohort (n = 546) by immunohistochemical (IHC) analysis of serial sections of a tissue microarray. Possible associations between methylation marks, routine clinicopathological parameters, ERG status, and biochemical recurrence (BCR) after RP were investigated.

Results

5mC and 5hmC levels were significantly reduced in PC compared to NM prostate tissue samples (p ≤ 0.027) due to a global loss of both marks specifically in ERG− PCs. 5fC levels were significantly increased in ERG+ PCs (p = 0.004), whereas 5caC levels were elevated in both ERG− and ERG+ PCs compared with NM prostate tissue samples (p ≤ 0.019). Positive correlations were observed between 5mC, 5fC, and 5caC levels in both NM and PC tissues (p < 0.001), while 5hmC levels were only weakly positively correlated to 5mC in the PC subset (p = 0.030). There were no significant associations between 5mC, 5fC, or ERG status and time to BCR in this RP cohort. In contrast, high 5hmC levels were associated with BCR in ERG− PCs (p = 0.043), while high 5caC levels were associated with favorable prognosis in ERG+ PCs (p = 0.011) and were borderline significantly associated with worse prognosis in ERG− PCs (p = 0.058). Moreover, a combined high-5hmC/high-5caC score was a significant adverse predictor of post-operative BCR beyond routine clinicopathological variables in ERG− PCs (hazard ratio 3.18 (1.54–6.56), p = 0.002, multivariate Cox regression).

Conclusions

This is the first comprehensive study of 5mC, 5hmC, 5fC, and 5caC levels in PC and the first report of a significant prognostic potential for 5caC in PC.

Electronic supplementary material

The online version of this article (10.1186/s13148-018-0540-x) contains supplementary material, which is available to authorized users.

RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer

Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.

Biomarker potential of ST6GALNAC3 and ZNF660 promoter hypermethylation in prostate cancer tissue and liquid biopsies

Current diagnostic and prognostic tools for prostate cancer (PC) are suboptimal, leading to overdiagnosis and overtreatment. Aberrant promoter hypermethylation of specific genes has been suggested as novel candidate biomarkers for PC that may improve diagnosis and prognosis. We here analyzed ST6GALNAC3 and ZNF660 promoter methylation in prostate tissues, and ST6GALNAC3,ZNF660,CCDC181, and HAPLN3 promoter methylation in liquid biopsies. First, using four independent patient sample sets, including a total of 110 nonmalignant (NM) and 705 PC tissue samples, analyzed by methylation‐specific qPCR or methylation array, we found that hypermethylation of ST6GALNAC3 and ZNF660 was highly cancer‐specific with areas under the curve (AUC) of receiver operating characteristic (ROC) curve analysis of 0.917–0.995 and 0.846–0.903, respectively. Furthermore, ZNF660 hypermethylation was significantly associated with biochemical recurrence in two radical prostatectomy (RP) cohorts of 158 and 392 patients and remained significant also in the subsets of patients with Gleason score ≤7 (univariate Cox regression and log‐rank tests, P < 0.05), suggesting that ZNF660 methylation analysis can potentially help to stratify low‐/intermediate‐grade PCs into indolent vs. more aggressive subtypes. Notably, ZNF660 hypermethylation was also significantly associated with poor overall and PC‐specific survival in the RP cohort (n = 158) with long clinical follow‐up available. Moreover, as proof of principle, we successfully detected highly PC‐specific hypermethylated circulating tumor DNA (ctDNA) for ST6GALNAC3,ZNF660,HAPLN3, and CCDC181 in liquid biopsies (serum) from 27 patients with PC vs. 10 patients with BPH, using droplet digital methylation‐specific PCR analysis. Finally, we generated a three‐gene (ST6GALNAC3/CCDC181/HAPLN3) ctDNA hypermethylation model, which detected PC with 100% specificity and 67% sensitivity. In conclusion, we here for the first time demonstrate diagnostic biomarker potential of ST6GALNAC3 and ZNF660 methylation, as well as prognostic biomarker potential of ZNF660. Furthermore, we show that hypermethylation of four genes can be detected in ctDNA in liquid biopsies (serum) from patients with PC.

RHCG and TCAF1 promoter hypermethylation predicts biochemical recurrence in prostate cancer patients treated by radical prostatectomy

PURPOSE

The lack of biomarkers that can distinguish aggressive from indolent prostate cancer has caused substantial overtreatment of clinically insignificant disease. Here, by genome-wide DNA methylome profiling, we sought to identify new biomarkers to improve the accuracy of prostate cancer diagnosis and prognosis.

EXPERIMENTAL DESIGN

Eight novel candidate markers, COL4A6, CYBA, TCAF1 (FAM115A), HLF, LINC01341 (LOC149134), LRRC4, PROM1, and RHCG, were selected from Illumina Infinium HumanMethylation450 BeadChip analysis of 21 tumor (T) and 21 non-malignant (NM) prostate specimens. Diagnostic potential was further investigated by methylation-specific qPCR analysis of 80 NM vs. 228 T tissue samples. Prognostic potential was assessed by Kaplan-Meier, uni- and multivariate Cox regression analysis in 203 Danish radical prostatectomy (RP) patients (cohort 1), and validated in an independent cohort of 286 RP patients from Switzerland and the U.S. (cohort 2).

RESULTS

Hypermethylation of the 8 candidates was highly cancer-specific (area under the curves: 0.79-1.00). Furthermore, high methylation of the 2-gene panel RHCG-TCAF1 was predictive of biochemical recurrence (BCR) in cohort 1, independent of the established clinicopathological parameters Gleason score, pathological tumor stage, and pre-operative PSA (HR (95% confidence interval (CI)): 2.09 (1.26 - 3.46); P = 0.004), and this was successfully validated in cohort 2 (HR (95% CI): 1.81 (1.05 - 3.12); P = 0.032).

CONCLUSION

Methylation of the RHCG-TCAF1 panel adds significant independent prognostic value to established prognostic parameters for prostate cancer and thus may help to guide treatment decisions in the future. Further investigation in large independent cohorts is necessary before translation into clinical utility.

Epigenetic alterations in TRAMP mice: epigenome DNA methylation profiling using MeDIP-seq

Purpose

We investigated the genomic DNA methylation profile of prostate cancer in transgenic adenocarcinoma of the mouse prostate (TRAMP) cancer model and to analyze the crosstalk among targeted genes and the related functional pathways.

Methods

Prostate DNA samples from 24-week-old TRAMP and C57BL/6 male mice were isolated. The DNA methylation profiles were analyzed by methylated DNA immunoprecipitation (MeDIP) followed by next-generation sequencing (MeDIP-seq). Canonical pathways, diseases and function and network analyses of the different samples were then performed using the Ingenuity^® Pathway Analysis (IPA) software. Some target genes with significant difference in methylation were selected for validation using methylation specific primers (MSP) and qPCR.

Results

TRAMP mice undergo extensive aberrant CpG hyper- and hypo-methylation. There were 2147 genes with a significant (log2-change ≥ 2) change in CpG methylation between the two groups, as mapped by the IPA software. Among these genes, the methylation of 1105 and 1042 genes was significantly decreased and increased, respectively, in TRAMP prostate tumors. The top associated disease identified by IPA was adenocarcinoma; however, the cAMP response element-binding protein (CREB)-, histone deacetylase 2 (HDAC2)-, glutathione S-transferase pi (GSTP1)- and polyubiquitin-C (UBC)-related pathways showed significantly altered methylation profiles based on the canonical pathway and network analyses. MSP and qPCR results of genes of interests corroborated with MeDIP-seq findings.

Conclusions

This is the first MeDIP-seq with IPA analysis of the TRAMP model to provide novel insight into the genome-wide methylation profile of prostate cancer. Studies on epigenetics, such as DNA methylation, will potentially provide novel avenues and strategies for further development of biomarkers targeted for treatment and prevention approaches for prostate cancer.

Promoter hypermethylation of SOX11 correlates with adverse clinicopathological features of human prostate cancer

Currently available tools for early diagnosis and prognosis of prostate cancer lack sufficient accuracy. There is a need to identify novel biomarkers for this common malignancy. SOX family genes play an important role in embryogenesis and are also implicated in various cancers. SOX11 has been recently recognized as a potential tumour suppressor that is downregulated in prostate cancer. We hypothesized that hypermethylation may be responsible for SOX11 silencing in human prostate cancer. The aim of the study was to investigate SOX11 promoter methylation in prostate adenocarcinoma by comparing it with benign prostatic hyperplasia (BPH). A total of 143 human prostate tissue samples, 62 from patients with prostate cancer and 81 from patients with BPH were examined by methylation-specific PCR. Associations between SOX11 promoter methylation and clinicopathological parameters were assessed by univariate statistics. Detection rates of SOX11 promoter methylation were 80.6% and 35.8% in prostate cancer and BPH respectively (P < 0.001). SOX11 hypermethylation was associated with adverse clinicopathological characteristics of prostate cancer, including higher PSA level (P < 0.01), Gleason score ≥ 7 (P = 0.03) and perineural invasion (P = 0.03). SOX11 methylation was positively correlated with the PSA level (P = 0.001). Our data indicated that SOX11 can be a promising methylation marker candidate for differential diagnosis and risk stratification for prostate cancer.

Clinical Significance of Measuring Global Hydroxymethylation of White Blood Cell DNA in Prostate Cancer: Comparison to PSA in a Pilot Exploratory Study

This is the first study investigating the clinical relevance of 5-hydroxymethylcytosine (5hmC) in genomic DNA from white blood cells (WBC) in the context of prostate cancer (PCa) and other prostate pathologies. Using an enzyme-linked immunosorbent assay, we identified significantly different distributions of patients with low and elevated 5hmC content in WBC DNA across controls and patients with prostate cancer (PCa), atypical small acinar proliferation (ASAP), and benign prostatic hyperplasia (BPH). The measured values were within the normal range for most PCa patients, while the latter category was predominant for ASAP. We observed a wider heterogeneity in 5hmC content in all of the prostate pathologies analyzed when compared to the healthy age-matched controls. When compared to blood levels of prostate-specific antigen (PSA), this 5hmC-based biomarker had a lower performance in PCa detection than the use of a PSA cut-off of 2.5 nanograms per milliliter (ng/mL). Above this threshold, however, it delineated almost three quarters of PCa patients from controls and patients with other prostate pathologies. Overall, genome-wide 5hmC content of WBC DNA appears to be applicable for detecting non-cancerous prostate diseases, rather than PCa. Our results also suggest a potential clinical usefulness of complementing PSA as a PCa marker by the addition of a set of hydroxymethylation markers in the blood, but further studies are necessary to confirm these findings.

Aberrant promoter methylation of the PAQR3 gene is associated with prostate cancer

Methylation markers are promising tools for diagnosis, prognosis and targeted treatment of cancer. In prostate carcinoma, aberrant promoter hypermethylation occurs earlier in the disease course and more consistently than recurrent somatic mutations. PAQR3, a tumor suppressor gene, was recently found to be downregulated in prostate cancer cell lines. We hypothesized that promoter methylation could be responsible for PAQR3 silencing in prostate cancer tissues. We aimed to investigate PAQR3 promoter methylation in prostate cancer by comparing it to benign prostatic hyperplasia (BPH). A total of 154 human prostate tissue samples, including 92 cases with prostate cancer and 62 cases with BPH, were examined by methylation-specific PCR. Clinicopathological correlation between PAQR3 promoter methylation and prognostically relevant variables was studied by statistical analysis. Promoter methylation of PAQR3 was significantly more frequent in prostate carcinoma compared to BPH (73.9% vs. 25.8%, p<0.01). The high prevalence of PAQR3 methylation in cancer foci was also confirmed with microdissection technique in 12 samples of prostate adenocarcinoma. PAQR3 hypermethylation was associated with perineural invasion (p=0.03), an adverse clinicopathological feature of prostate cancer. We concluded that PAQR3 can be a promising methylation marker candidate for the detection and monitoring of prostate cancer.

DNA Methylation and Urological Cancer, a Step Towards Personalized Medicine: Current and Future Prospects

Urologic malignancies are some of the commonest tumors often curable when diagnosed at early stage. However, accurate diagnostic markers and faithful predictors of prognosis are needed to avoid over-diagnosis leading to overtreatment. Many promising exploratory studies have identified epigenetic markers in urinary malignancies based on DNA methylation, histone modification and non-coding ribonucleic acid (ncRNA) expression that epigenetically regulate gene expression. We review and discuss the current state of development and the future potential of epigenetic biomarkers for more accurate and less invasive detection of urological cancer, tumor recurrence and progression of disease serving to establish diagnosis and monitor treatment efficacies. The specific clinical implications of such methylation tests on therapeutic decisions and patient outcome and current limitations are also discussed.

Aberrant Promoter Methylation of Protocadherin8 (PCDH8) in Serum is a Potential Prognostic Marker for Low Gleason Score Prostate Cancer

Background

PCDH8 is a newly-discovered suppressor gene that is frequently inactivated by aberrant methylation in several human cancers, including prostate cancer. The identification of PCDH8 methylation can be used as a potential predictive biomarker. Prostate cancer patients with high Gleason score are considered as being at high risk for tumor recurrence and progression, and adjuvant therapy is often routinely performed in clinical practice. In the present study, we did not measure the methylation of PCDH8 in these patients. The main purpose of the present study was to evaluate the clinical significance of PCDH8 methylation in serum of prostate cancer patients with low Gleason score.

Material/Methods

PCDH8 methylation in serum samples of 117 patients and 47 controls was checked by methylation-specific PCR (MSP). Then, we correlated PCDH8 methylation status with the clinicopathological parameters of prostate cancer patients with low Gleason score and patient outcomes.

Results

We found that PCDH8 was more frequently methylated in serum samples of patients with prostate cancer than in controls. PCDH8 methylation was correlated with advanced clinical stage (P=0.021), higher level of preoperative PSA (P=0.008), and positive lymph node metastasis (P=0.010). Moreover, patients with PCDH8 methylation had worse biochemical recurrence (BCR)-free survival (P<0.001) than patients without. Independent prognostic factors for worse BCR-free survival of prostate cancer patients with low Gleason score were: PCDH8 methylation in serum (Exp (B)=3.147, 95% CI: 1.152–7.961, P=0.007), clinical stage (Exp (B)=2.53, 95% CI: 1.032–4.763, P=0.025) and lymph node status (Exp (B)=1.476, 95% CI: 1.107–4.572, P=0.042).

Conclusions

Our study indicated that PCDH8 methylation in serum occurred frequently in prostate cancer patients and was correlated with risk factors for poor outcome. The methylation of PCDH8 in serum is a potential predictive marker for prostate cancer patients with low Gleason score after surgery.

Comprehensive Evaluation of TFF3 Promoter Hypomethylation and Molecular Biomarker Potential for Prostate Cancer Diagnosis and Prognosis

Overdiagnosis and overtreatment of clinically insignificant tumors remains a major problem in prostate cancer (PC) due to suboptimal diagnostic and prognostic tools. Thus, novel biomarkers are urgently needed. In this study, we investigated the biomarker potential of Trefoil factor 3 (TFF3) promoter methylation and RNA expression levels for PC. Initially, by quantitative methylation specific PCR (qMSP) analysis of a large radical prostatectomy (RP) cohort (n = 292), we found that the TFF3 promoter was significantly hypomethylated in PC compared to non-malignant (NM) prostate tissue samples (p < 0.001) with an AUC (area under the curve) of 0.908 by receiver operating characteristics (ROC) curve analysis. Moreover, significant TFF3 promoter hypomethylation (p ≤ 0.010) as well as overexpression (p < 0.001) was found in PC samples from another large independent patient sample set (498 PC vs. 67 NM) analyzed by Illumina 450K DNA methylation arrays and/or RNA sequencing. TFF3 promoter methylation and transcriptional expression levels were inversely correlated, suggesting that epigenetic mechanisms contribute to the regulation of gene activity. Furthermore, low TFF3 expression was significantly associated with high ERG, ETS transcription factor (ERG) expression (p < 0.001), as well as with high Gleason score (p < 0.001), advanced pathological T-stage (p < 0.001), and prostate-specific antigen (PSA) recurrence after RP (p = 0.013; univariate Cox regression analysis). There were no significant associations between TFF3 promoter methylation levels, ERG status, or PSA recurrence in these RP cohorts. In conclusion, our results demonstrated diagnostic biomarker potential of TFF3 promoter hypomethylation for PC as well as prognostic biomarker potential of TFF3 RNA expression. To the best of our knowledge, this is the most comprehensive study of TFF3 promoter methylation and transcriptional expression in PC to date.

The relative contribution of DNA methylation and genetic variants on protein biomarkers for human diseases

Associations between epigenetic alterations and disease status have been identified for many diseases. However, there is no strong evidence that epigenetic alterations are directly causal for disease pathogenesis. In this study, we combined SNP and DNA methylation data with measurements of protein biomarkers for cancer, inflammation or cardiovascular disease, to investigate the relative contribution of genetic and epigenetic variation on biomarker levels. A total of 121 protein biomarkers were measured and analyzed in relation to DNA methylation at 470,000 genomic positions and to over 10 million SNPs. We performed epigenome-wide association study (EWAS) and genome-wide association study (GWAS) analyses, and integrated biomarker, DNA methylation and SNP data using between 698 and 1033 samples depending on data availability for the different analyses. We identified 124 and 45 loci (Bonferroni adjusted P < 0.05) with effect sizes up to 0.22 standard units' change per 1% change in DNA methylation levels and up to four standard units' change per copy of the effective allele in the EWAS and GWAS respectively. Most GWAS loci were cis-regulatory whereas most EWAS loci were located in trans. Eleven EWAS loci were associated with multiple biomarkers, including one in NLRC5 associated with CXCL11, CXCL9, IL-12, and IL-18 levels. All EWAS signals that overlapped with a GWAS locus were driven by underlying genetic variants and three EWAS signals were confounded by smoking. While some cis-regulatory SNPs for biomarkers appeared to have an effect also on DNA methylation levels, cis-regulatory SNPs for DNA methylation were not observed to affect biomarker levels. We present associations between protein biomarker and DNA methylation levels at numerous loci in the genome. The associations are likely to reflect the underlying pattern of genetic variants, specific environmental exposures, or represent secondary effects to the pathogenesis of disease.