Genome-Wide DNA Methylation Model for the Diagnosis of Prostate Cancer

Abstract

Prostate cancer is the most prevalent and the second most lethal malignancy among males in the United States of America. Its diagnosis is almost entirely predicated upon histopathological analysis of the biopsied tissue, and it is associated with a substantial average error. Using genome-wide DNA methylation data derived from 469 prostatic tumor tissue samples and 50 normal prostatic tissue samples and interrogating over 485 000 CpG sites per sample (spanning across gene promoters, CpG islands, shores, shelves, gene bodies, and intergenic and other areas), we were able to develop a mathematical model that classified with a high accuracy (overall sensitivity = 95.31% and overall specificity = 94.00%) tumor tissue versus normal tissue. The methylation β values of five CpG sites, corresponding to the genes LINC01091, RPS15, SNORA10, and two unknown DNA areas in chromosome 1, provided the input to the model. The model was validated with unknown samples, as well as with a sixfold cross-validation and a leave-one-out cross-validation. This study presents a novel genomic model based on genome-wide DNA methylation analysis of biopsied prostatic tissue that could aid in the diagnosis of prostate cancer and help advance the transition to genomic medicine.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

“…We further assessed significant methylation–gene expression associations identified in blood tissue analyses in adjacent normal prostate tissue of PrCa patients in the TCGA ( N = 34). The processing of DNA methylation and gene expression data has been described elsewhere 62 , 63 .…”

Differential DNA Methylation in Prostate Tumors from Puerto Rican Men

“…Our study is not the first to identify differences in methylation patterns between tumor and adjacent normal tissue in PCa. Using epigenome-wide 450K DNA methylation data derived from 469 PCa tumors and 50 normal prostatic tissue samples, Nikas and Nikas (2019) were able to develop a mathematical model that classified prostate tumor tissue versus normal tissue with a high sensitivity (95.3%) and specificity (94.0%) [ 8 ]. Since this group only compared tumor with adjacent normal tissues, their results cannot reflect differences within prostate tumors (aggressive and indolent).…”

“…This is associated with an average error of 25–30% in the case of under-detection and an average error of 1.3–7.1% in the case of over-detection [ 5 , 6 ]. The accuracy of a Gleason score is estimated to be 61% [ 7 , 8 ]. Although PCa is normally characterized by a slow progression, about 20–30% of cases are associated with an aggressive phenotype that could lead to metastasis and death.…”

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

“…In the last decades, whole genome strategies have dramatically expanded the identification of aberrantly methylated genes [15,47], allowing systematic approaches that seek for maximum accuracy by the generation of diagnostic signatures. Thus, in addition to classical biomarkers of diagnosis based on single methylated genes [43,48], some specific DNA-methylation-based signatures have been described for the detection of several tumours, such as prostate cancer [49,50], hepatocellular carcinoma [51], and glioma [52]. However, virtually no diagnostic biomarker or signature based on DNA methylation has been proposed for TNBC.…”