Integrating miRNA profiling and machine learning for improved prostate cancer diagnosis.

ABSTRACT: Prostate cancer (PCa) diagnosis remains challenging due to overlapping clinical features with benign prostatic hyperplasia (BPH) and limitations of existing diagnostic tools like PSA tests, which yield high false-positive rates. This study investigates the potential of microRNA (miRNA) biomarkers, analyzed via reverse transcription polymerase chain reaction and machine learning (ML), to enhance diagnostic accuracy. miRNAs such as miR-21-5p, miR-141-3p, and miR-221-3p were identified as significant discriminators between PCa and BPH through a prospective cohort study. Whole blood miRNA profiling offered a robust systemic representation of disease states. A random forest ML model was trained on expression data, achieving notable performance metrics: an accuracy of 77.42%, AUC of 0.78 during

SUBMITTER: Singh S 

PROVIDER: S-EPMC12365097 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature