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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.

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