We then constructed a receiver operating characteristic curve (ROC) based on a random forest algorithm and calculated the area under it (i.e., the AUC), where the results showed SERPINA3, LRG1, and SCGB3A1 added classified value (added AUC; 0.09, 0.02, 0.04, respectively) in addition to age, prostate size, BMI, and PSA, suggesting the clinical value by incorporating EV biomarkers in prostate cancer diagnosis (Figure 5d). The gene discussed is SERPINA3; the disease is Familial prostate cancer.