To assess the diagnostic performance of semen RNA-based biomarkers for detecting prostate cancer and differentiating cancer grade groups.
In a multi-center prospective study, semen samples were collected from men prior to undergoing prostate biopsy. RNA was extracted and sequenced to generate exome-wide gene expression profiles. Differentially expressed genes were selected and used to train a machine-learning classifier designed to detect prostate cancer with ≥95% sensitivity. The finalized model was locked and independently evaluated in a validation cohort. Histopathological diagnosis served as the reference standard. Model performance was further analyzed in relation to ISUP Grade Group classification.
Of 301 enrolled participants, 279 samples met quality criteria and were included in model development (training set: n = 199; validation set: n = 80). The median age and PSA level were 62 years and 5.70 ng/mL, respectively. In the validation cohort, the classifier achieved an AUC of 0.90, sensitivity of 92%, and specificity of 69%, with no significant performance difference compared to the training cohort. Importantly, high-risk cancers (ISUP Grade Group ≥3) were ruled out with a negative predictive value of 96% in validation.
This study demonstrates that non-invasive, high-accuracy prostate cancer tests can be developed using semen samples collected at home. The proposed test could potentially eliminate 60-70% of unnecessary biopsies, representing a substantial improvement in prostate cancer testing and risk stratification.
The Journal of urology. 2026 May 13 [Epub ahead of print]
Duncan H Whitney, Matthew Clay, Joel Wipperfurth, Dennis Wylie, Neal D Shore, Richard D'Anna, Daniel Saltzstein, Kayli Little, Mohsen Nabian, Daniel H Hovelson, Emily Breunig, Michael Brawer, Lauren Tyra, Tobias Zutz, David Jarrard
Gregor Diagnostics (Madison, WI)., Center for Biomedical Research Support, Bioinformatics Consulting Group, University of Texas at Austin (Austin, TX)., Carolina Urologic Research Center (Myrtle Beach, SC)., Arkansas Urology (Little Rock, AK)., Urology San Antonio (San Antonio, TX)., Department of Pathology, Division of Diagnostic Genetics and Genomics, University Michigan (Ann Arbor, MI)., Department of Urology, University of Wisconsin School of Medicine and Public Health and University of Wisconsin Carbone Cancer Center (Madison, WI).