Machine Learning Accurately Predicts Muscle Invasion of Bladder Cancer Based on Three miRNAs.

ABSTRACT: The aim of this study was to validate the diagnostic potential of four previously identified miRNAs in two independent cohorts and to develop accurate classification models to predict invasiveness of bladder cancer. Furthermore, molecular subtypes were investigated. The miRNAs were isolated from pTa low-grade (lg) (n = 113), pT1 high-grade (hg) (n = 133) and muscle-invasive bladder cancer (MIBC) (n = 136) tumour tissue samples (FFPE) after either transurethral resection of a bladder tumour (TURB) or cystectomy (CYS). In both cohorts, the expression of miR-138-5p and miR-200a-3p was significantly lower, and the expression of miR-146b-5p and miR-155-5p was significantly higher in MIBC compared to pTa lg. A k-nearest neighbours (KNN) classifier trained to distinguish pTa lg from MIBC based on

SUBMITTER: Eckhart L 

PROVIDER: S-EPMC11810526 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature