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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiyi Pu ◽  
Chao Li ◽  
Haining Yuan ◽  
Xiaoju Wang
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
In Silico ◽  
Prostate Cancer Screening ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Translation Rate ◽  
Normal Prostate ◽  
Cancer Data ◽  
Significant Difference

Abstract 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.

scholarly journals Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature

2021 ◽  
Vol 9 ◽  
Author(s):  
Enchong Zhang ◽  
Fujisawa Shiori ◽  
Oscar YongNan Mu ◽  
Jieqian He ◽  
Yuntian Ge ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Immune Cell ◽  
Methylation Status ◽  
Disease Status ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cancer Subtypes ◽  
Cpg Sites ◽  
Selection Operator

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.

scholarly journals Aberrant Hypermethylation-Mediated Suppression of PYCARD Is Extremely Frequent in Prostate Cancer with Gleason Score ≥ 7

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Toshiya Miyauchi ◽  
Masahiro Takahashi ◽  
Koji Mitsuzuka ◽  
Yuriko Saiki ◽  
Teppei Okubo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Gleason Score ◽  
Transcriptional Activation ◽  
Methylation Status ◽  
Promoter Hypermethylation ◽  
Aberrant Methylation ◽  
Basal Cells ◽  
Normal Prostate ◽  
Induced Apoptosis

Epigenetic gene silencing by aberrant DNA methylation leads to loss of key cellular pathways in tumorigenesis. In order to analyze the effects of DNA methylation on prostate cancer, we established LNCaP-derived human prostate cancer cells that can pharmacologically induce global reactivation of hypermethylated genes by the methyl-CpG targeted transcriptional activation (MeTA) method. The MeTA suppressed the growth of LNCaP-derived cells and induced apoptosis. Microarray analysis indicated that PYCARD (PYD and CARD domain containing) encoding an apoptosis-inducing factor was upregulated by 65-fold or more after treatment with MeTA. We analyzed DNA methylation statuses using 50 microdissected primary prostate cancer tissues and found an extremely high frequency of tumor-specific promoter hypermethylation of PYCARD (90%, 45/50). Moreover, DNA methylation status was significantly associated with Gleason score ( P = 0.0063 ); the frequency of tumor-specific hypermethylation was 96% (44/46) in tumors with Gleason score ≥ 7 , whereas that in tumors with Gleason score 6 was 25% (1/4). Immunohistochemical analyses using these 50 cases indicated that only 8% (4/50) of cancerous tissues expressed PYCARD, whereas 80% (40/50) of corresponding normal prostate epithelial and/or basal cells expressed PYCARD. In addition, there was no relationship between PYCARD immunostaining and the Gleason score in cancerous tissue and surrounding normal tissue. Inducible expression of PYCARD inhibited cell proliferation by induction of apoptosis. These results suggest that aberrant methylation of PYCARD is a distinctive feature of prostate cancers with Gleason score ≥ 7 and may play an important role in escaping from apoptosis in prostatic tumorigenesis.

scholarly journals MYC DNA Methylation in Prostate Tumor Tissue is Associated with Gleason Score

Genes ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Kathryn Hughes Barry ◽  
Kareshma Mohanty ◽  
Patricia A. Erickson ◽  
Difei Wang ◽  
Jianxin Shi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Gleason Score ◽  
Medical Center ◽  
Prostate Tumor ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Normal Prostate ◽  
Cpg Sites ◽  
Normal Prostate Tissue

Increasing evidence suggests a role of epigenetic mechanisms at chromosome 8q24, an important cancer genetic susceptibility region, in prostate cancer. We investigated whether MYC DNA methylation at 8q24 (six CpG sites from exon 3 to the 3′ UTR) in prostate tumor was associated with tumor aggressiveness (based on Gleason score, GS), and we incorporated RNA expression data to investigate the function. We accessed radical prostatectomy tissue for 50 Caucasian and 50 African American prostate cancer patients at the University of Maryland Medical Center, selecting an equal number of GS 6 and GS 7 cases per group. MYC DNA methylation was lower in tumor than paired normal prostate tissue for all six CpG sites (median difference: −14.74 to −0.20 percentage points), and we observed similar results for two nearby sites in The Cancer Genome Atlas (p < 0.0001). We observed significantly lower methylation for more aggressive (GS 7) than less aggressive (GS 6) tumors for three exon 3 sites (for CpG 212 (chr8:128753145), GS 6 median = 89.7%; GS 7 median = 85.8%; p-value = 9.4 × 10−4). MYC DNA methylation was not associated with MYC expression, but was inversely associated with PRNCR1 expression after multiple comparison adjustment (q-value = 0.04). Findings suggest that prostate tumor MYC exon 3 hypomethylation is associated with increased aggressiveness.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

scholarly journals Analysis of PMEPA1 Isoforms (a and b) as Selective Inhibitors of Androgen and TGF-β Signaling Reveals Distinct Biological and Prognostic Features in Prostate Cancer

Cancers ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1995
Author(s):  
Shashwat Sharad ◽  
Zsófia M. Sztupinszki ◽  
Yongmei Chen ◽  
Claire Kuo ◽  
Lakshmi Ravindranath ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Proliferation ◽  
Transmembrane Protein ◽  
The Cancer Genome Atlas ◽  
Cell Counting ◽  
Luciferase Reporter ◽  
Mrna Levels ◽  
Normal Prostate ◽  
Prostate Tumorigenesis ◽  
Prognostic Features

Dysfunctions of androgen/TGF-β signaling play important roles in prostate tumorigenesis. Prostate Transmembrane Protein Androgen Induced 1 (PMEPA1) inhibits androgen and TGF-β signaling via a negative feedback loop. The loss of PMEPA1 confers resistance to androgen signaling inhibitors and promotes bone metastasis. Conflicting reports on the expression and biological functions of PMEPA1 in prostate and other cancers propelled us to investigate isoform specific functions in prostate cancer (PCa). One hundred and twenty laser capture micro-dissection matched normal prostate and prostate tumor tissues were analyzed for correlations between quantitative expression of PMEPA1 isoforms and clinical outcomes with Q-RT-PCR, and further validated with a The Cancer Genome Atlas (TCGA) RNA-Seq dataset of 499 PCa. Cell proliferation was assessed with cell counting, plating efficiency and soft agar assay in androgen responsive LNCaP and TGF-β responsive PC3 cells. TGF-β signaling was measured by SMAD dual-luciferase reporter assay. Higher PMEPA1-a mRNA levels indicated biochemical recurrence (p = 0.0183) and lower PMEPA1-b expression associated with metastasis (p = 0.0173). Further, lower PMEPA1-b and a higher ratio of PMEPA1-a vs. -b were correlated to higher Gleason scores and lower progression free survival rate (p < 0.01). TGF-β-responsive PMEPA1-a promoted PCa cell growth, and androgen-responsive PMEPA1-b inhibited cancer cell proliferation. PMEPA1 isoforms -a and -b were shown to be promising candidate biomarkers indicating PCa aggressiveness including earlier biochemical relapse and lower disease specific life expectancy via interrupting androgen/TGF-β signaling.

scholarly journals Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096357
Author(s):  
Xiaoyong Gong ◽  
Bobin Ning
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Prostate ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

scholarly journals Validation of an epigenetic field of susceptibility to detect significant prostate cancer from non-tumor biopsies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Bing Yang ◽  
Tyler Etheridge ◽  
Johnathon McCormick ◽  
Adam Schultz ◽  
Tariq A. Khemees ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Prostate Biopsy ◽  
Cancer Susceptibility ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Grade Group ◽  
Normal Prostate ◽  
Normal Prostate Tissue ◽  
Multifocal Disease

Abstract Background An epigenetic field of cancer susceptibility exists for prostate cancer (PC) that gives rise to multifocal disease in the peripheral prostate. In previous work, genome-wide DNA methylation profiling identified altered regions in the normal prostate tissue of men with PC. In the current multicenter study, we examined the predictive strength of a panel of loci to detect cancer presence and grade in patients with negative biopsy tissue. Results Four centers contributed benign prostate biopsy tissues blocks from 129 subjects that were either tumor associated (TA, Grade Group [GG] ≥ 2, n = 77) or non-tumor associated (NTA, n = 52). Biopsies were analyzed using pyrosequencing for DNA methylation encompassing CpG loci near CAV1, EVX1, FGF1, NCR2, PLA2G16, and SPAG4 and methylation differences were detected within all gene regions (p < 0.05). A multiplex regression model for biomarker performance incorporating a gene combination discriminated TA from NTA tissues (area under the curve [AUC] 0.747, p = 0.004). A multiplex model incorporating all the above genes and clinical information (PSA, age) identified patients with GG ≥ 2 PC (AUC 0.815, p < 0.0001). In patients with cancer, increased variation in gene methylation levels occurs between biopsies across the prostate. Conclusions A widespread epigenetic field defect is utilized to detect GG ≥ 2 PC in patients with histologically negative biopsies. These alterations in non-tumor cells display increased heterogeneity of methylation extent and are spatially distant from tumor foci. These findings have the potential to decrease the need for repeated prostate biopsy.

scholarly journals Frequent DNA Hypermethylation at the RASSF1A and APC Gene Loci in Prostate Cancer Patients of Pakistani Origin

ISRN Urology ◽  
2013 ◽  
Vol 2013 ◽  
pp. 1-4
Author(s):  
Ahmed Yaqinuddin ◽  
Sohail A. Qureshi ◽  
Shahid Pervez ◽  
Mohammed Umair Bashir ◽  
Ressam Nazir ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Methylation Status ◽  
Transurethral Resection Of Prostate ◽  
Gene Promoters ◽  
Dna Hypermethylation ◽  
Pakistani Population ◽  
Specific Pcr ◽  
Pakistani Origin

DNA methylation has emerged as a potentially robust biomarker for prostate cancer (PCa). Since DNA methylomes appear to be disease as well as population specific, we have assessed the DNA methylation status of RASSF1A, APC, and p16 (potential biomarkers of PCa) in Pakistani population. Primary prostate cancer tissues were obtained from 27 formalin-fixed paraffin-embedded blocks (FFPE) of cancer patients who underwent radical prostatectomy and transurethral resection of prostate (TURP) during 2003–2008. As controls, twenty-four benign prostatic FFPE tissues were obtained from patients who underwent TURP for benign prostatic hyperplasia during 2008. DNA was extracted, and methylation-specific PCR was used to assess the methylation status for RASSF1A, APC, and p16 gene promoters. Our results revealed that the RASSF1A promoter was hypermethylated in all the tested cancer samples but was also hypermethylated in 3 out of 24 control tissues. The APC promoter was hypermethylated in 15 out of 27 cancer samples and in none of the control samples. Strikingly, none of the samples showed methylation at the p16 promoter. Our findings suggest that RASSF1A and APC gene promoters are frequently hypermethylated in the Pakistani population and therefore have the potential to develop into universally dependable biomarkers for detecting PCa.

scholarly journals Increased Sensitivity of Detection of RASSF1A and GSTP1 DNA Fragments in Serum of Prostate Cancer Patients: Optimisation of Diagnostics Using OBBPA-ddPCR

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4459
Author(s):  
Markus Friedemann ◽  
Friederike Horn ◽  
Katharina Gutewort ◽  
Lars Tautz ◽  
Carsten Jandeck ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
Methylation Status ◽  
High Signal ◽  
Tumour Suppressor Genes ◽  
Analytical Sensitivity ◽  
Grey Zone ◽  
Dna Fragments ◽  
Step Method ◽  
Rassf1a Methylation

Identification of aberrant DNA methylation is a promising tool in prostate cancer (PCa) diagnosis and treatment. In this study, we evaluated a two-step method named optimised bias-based preamplification followed by digital PCR (OBBPA-dPCR). The method was used to identify promoter hypermethylation of 2 tumour suppressor genes RASSF1A and GSTP1 in the circulating cell-free DNA (cfDNA) from serum samples of PCa patients (n = 75), benign prostatic hyperplasia (BPH, n = 58), and healthy individuals (controls, n = 155). The PCa cohort was further subdivided into subgroups comprising (I) patients with Gleason Scores (GS) ≤ 7 (n = 55), (II) GS ≥ 8 (n = 10), and (III) patients with metastatic PCa diagnosis (n = 10). We found that RASSF1A methylation levels were significantly increased in all 3 PCa subgroups compared to the controls and BPH cohorts (p < 0.01 for all comparisons). Fractional abundances of methylated GSTP1 DNA fragments were significantly increased in subgroup III of metastatic PCa patients (p < 0.001). RASSF1A methylation analysis was found to be beneficial as a complementary biomarker where further diagnostic validation is most crucial. In combination with free PSA, RASSF1A methylation status helps to identify PCa patients with GS ≥ 8 and grey-zone total PSA values between 2–10 ng/mL. In our study, PCR biases between 80–90% were sufficient to detect minute amounts of tumour DNA with high signal-to-noise ratios as well as high analytical sensitivity and specificity. Both RASSF1A and GSTP1 exhibited strongly increased DNA methylation levels in all metastatic PCa patients. Our data indicates a superior sensitivity of epigenetic biomarker analyses in early detection of PCa metastases that should also help to improve PCa therapy.

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