부산대학교 도서관

Summary To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance.
Highlights •An overnight urine sample is collected for lncRNA testing•An lncRNA diagnostic model for detecting high-grade prostate cancer is developed•This model can identify high-grade cancer in patients with an equivocal PSA range•This model can assist in the selection of candidates for active surveillance
Tao et al. identify and validate a series of urine long non-coding RNA markers, which can assist in the accurate diagnosis of high-grade prostate cancer. In addition, these urine RNA markers are also useful for the selection of candidates with prostate cancer for active surveillance.