Urine Biomarker Panel Sensitive, Specific for Prostate Cancer Diagnosis
THURSDAY, Sept. 11, 2025 -- A urine-based biomarker panel has high accuracy for diagnosing prostate cancer (PCa), according to a study published in the September issue of eBioMedicine.
Menglang Yuan, M.D., Ph.D., from Johns Hopkins University in Baltimore, and colleagues identified and validated a panel of urinary RNA biomarkers to examine the diagnostic accuracy for PCa. Fifty candidate RNAs were identified in an RNA-sequencing analysis of exfoliated cells in urine specimens; three biomarkers with optimal specificity and sensitivity were selected as a biomarker panel (TTC3, H4C5, and EPCAM). Diagnostic performance of the panel was examined in a case-control study, which included development and validation datasets (243 and 646 participants, respectively).
The researchers found that the urine panel robustly identified PCa with an area under the curve (AUC) of 0.96 compared with 0.83 for urinary prostate cancer antigen 3 RNA in the development dataset; for the validation dataset, the corresponding AUCs were 0.92 and 0.76, respectively. Postprostatectomy, urine biomarkers were nearly eliminated and were confirmed to originate from prostate tissue at both the RNA and protein levels. High diagnostic accuracy of prostate-specific antigen-negative PCa cases was maintained with the panel, and it could differentiate PCa from benign prostate conditions. In vivo and in vitro tumor growth was suppressed with TTC3 depletion.
"This test has the potential to help physicians improve diagnostic accuracy of prostate cancer, reducing unnecessary interventions while allowing early treatment for those who need it," coauthor Vipul Patel, M.D., from AdventHealth Cancer Institute in Celebration, Florida, said in a statement.
Several authors disclosed ties to the pharmaceutical industry.
Disclaimer: Statistical data in medical articles provide general trends and do not pertain to individuals. Individual factors can vary greatly. Always seek personalized medical advice for individual healthcare decisions.
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