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Updated: Oct 7, 2025

Cell-Free DNA Integrity Analysis in Urine Samples
Published on: January 5, 2017
Epigenetic field alterations in non-tumor prostate tissues detect prostate cancer in urine
Tariq A Khemees1, Bing Yang1, Adam Schultz1
1Department of Urology, School of Medicine and Public Health, University of Wisconsin-Madison Madison 53705, WI, USA.
Abstract:
Prostate cancer (PC) development involves epigenetic DNA methylation changes that occur in the tumor. However, distinct DNA methylation changes have been previously found to encompass a widespread cancer field defect involving normal prostate tissue. In the current study, we analyzed a series of DNA methylation field markers to determine if they predict the presence of PC in urine. Urine samples were collected from patients undergoing prostate biopsy with biopsy-proven PC (90), and without PC (77). From the urine pellet, methylated DNA was quantified across several previously identified CpG island regions near the caveolin 1 (CAV1), even-skipped homeobox 1 (EVX1), fibroblast growth factor 1 (FGF1), natural cytotoxicity triggering receptor 2 (NCR2) and phospholipase A and acyltransferase 3 (PLA2G16) genes using bisulfite pyrosequencing. Univariate and multivariate analyses were performed. Urine cell pellets show significant increases in methylation in four of the markers from patients with PC compared to those without PC including EVX1 12.2 vs. 7.7%, CAV1 15.7 vs. 10.36%, FGF1 12.0 vs. 7.1%, and PLA2G16 12.2 vs. 8.3% [all P<0.01]. Area under the ROC Curve (AUCs) were generated for EXV1 (0.74, Odds ratios (OR) 1.09; 95% confidence intervals (CI) 0.94-1.25, CAV1 (0.72, OR 1.18; 95% CI 1.09-1.28) and PLA2G16 (0.76, OR 1.35; 95% CI 1.199-1.51). In combination, a two-marker assay performs better than prostate specific antigen (PSA), AUC 0.77 vs. PSA AUC of 0.6 (P = 0.01) with the lowest error. In addition, FGF1 distinguished between grade group 1 (GG1) and higher grade cancers (P<0.03). In conclusion, applying methylation of field defect loci to urine samples provides a novel approach to distinguish patients with and without cancer.

