Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.

ABSTRACT:

Background

Motor proteins play significant roles in cancer progression, but their involvement in biochemical recurrence (BCR) of prostate cancer remains unclear. The objective of the study is to develop a prognostic indicator for BCR using machine learning (ML) and motor protein-related genes (MPRGs).

Methods

The prognosis relevance of the MPRGs in prostate cancer was analyzed by univariate Cox regression. Feature selection and model construction were performed using combinations of multiple machine learning algorithms. Model performance was assessed using receiver operating characteristic curve and C-index. Patients were stratified into high- and low-risk groups based on the risk signature, and comparisons of BCR incidence, gene expression profiles, immune cell infiltration

SUBMITTER: Wang W 

PROVIDER: S-EPMC12440175 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature