Abstract Emerging evidence suggests that prostatic secretions are a promising source of cancer biomarkers. Approximately 30% of semen originates from the prostate and contains nucleic acids and proteins derived from both cancerous and precursor epithelial cells. This novel study is the first 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 91. 7%, and specificity of 68. 8%, 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 99%. 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 up to 70% of unnecessary biopsies, representing a substantial improvement in prostate cancer screening and risk stratification. i Citation Format: David jarrard, Duncan Whitney, Matthew Clay, Dennis Wylie, Neal Shore, Daniel Saltzstein, Daviel Hovelson, Emily Breunig, Michael Brawer, Lauren Tyra, Tobias Zutz. Development and validation of a semen-RNA based classifier for detection and risk stratification of prostate cancer abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (2Suppl): Abstract nr PR002.
jarrard et al. (Tue,) studied this question.