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Updated: Jan 4, 2026
Methyl-binding DNA capture Sequencing for Patient Tissues
Published on: October 31, 2016
DNA methylation in repeat negative prostate biopsies as a marker of missed prostate cancer
Valentina Fiano1, Daniela Zugna2, Chiara Grasso2
1Cancer Epidemiology Unit-CeRMS, Department of Medical Sciences, University of Turin and CPO Piemonte, Via Santena 7, 10126, Turin, Italy. valentina.fiano@unito.it.
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.
