Abstract
In the quest for efficient tumor diagnosis via liquid biopsy, extracellular vesicles (EVs) have shown promise as a source of potential biomarkers. This study addresses the gap in biomarker efficacy for predicting clinically significant prostate cancer (csPCa) between the Western and Chinese populations. We developed a urinary extracellular vesicles-based prostate score (EPS) model, utilizing the EXODUS technique for EV isolation from 598 patients and incorporating gene expressions of FOXA1, PCA3, and KLK3. Our findings reveal that the EPS model surpasses prostate-specific antigen (PSA) testing in diagnostic accuracy within a training cohort of 234 patients, achieving an area under the curve (AUC) of 0.730 compared to 0.659 for PSA (p = 0.018). Similarly, in a validation cohort of 101 men, the EPS model achieved an AUC of 0.749, which was significantly better than PSA's 0.577 (p < 0.001). Our model has demonstrated a potential reduction in unnecessary prostate biopsies by 26%, with only a 3% miss rate for csPCa cases, indicating its effectiveness in the Chinese population.