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Prostate Cancer (PRC) training cohort for small RNA characterization

ABSTRACT: Prostate Cancer (PRC) training cohort for small RNA characterization

PROVIDER: PRJEB38300 | ENA |

REPOSITORIES: ENA

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Prostate Cancer (PRC) training cohort for small RNA characterization

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Project description:Background: New molecular biomarkers for prostate cancer (PC) prognosis are urgently needed. Ratio-based models are attractive, as they require no additional normalization. Here, we train and independently validate a novel 4-miRNA prognostic ratio model for PC. Patients and methods: By genome-wide miRNA expression profiling of PC tissue samples from 123 men who underwent radical prostatectomy (RP) (PCA123, training cohort), we identified six top candidate prognostic miRNAs and systematically tested their ability to predict post-operative biochemical recurrence (BCR). The best miRNA-based prognostic ratio model (MiCaP) was validated in two independent cohorts (PCA352 and PCA476) including >800 RP patients in total. Clinical endpoints were BCR and prostate cancer-specific survival (CSS). The prognostic potential of MiCaP was assessed by univariate and multivariate Cox-regression analyses and Kaplan-Meier analyses. Results: We identified a 4-miRNA ratio model, MiCaP (miR-23a-3p×miR-10b-5p)/(miR-133a×miR-374b-5p), that predicted time to BCR independently of routine clinicopathological variables in the training cohort (PCA123) and was successfully validated in two independent RP cohorts. In addition, MiCaP was a significant predictor of CSS in univariate analysis (HR 3·35 [95% CI 1·34 – 8·35], P=0·0096) and in multivariate analysis (HR 2·43 [95% CI 1·45 – 4·07], P=0·0210). Limitations include low mortality rates (CSS: 5.4%). Conclusions: We identified a novel 4-miRNA ratio model (MiCaP) with significant independent prognostic value in three RP cohorts, indicating promising potential to improve PC risk stratification.

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