Senescence-related gene signature predicts prostate cancer progression and identifies PCNA as a therapeutic target via multi-omics machine learning integration.

ABSTRACT:

Background

Senescence plays a critical role in prostate cancer, influencing disease onset and progression. However, the alterations of senescence-associated genes during prostate cancer progression and their potential value in predicting disease advancement remain to be further elucidated.

Methods

117 machine learning methods were applied to construct the senescence-related gene signature (SRGS). Temporal trajectory analysis based on bulk and single-cell transcriptomic datasets was performed to link SRGS with prostate cancer progression. Functional validations of PCNA were conducted both in vitro and in vivo to support our analytical findings.

Results

Using 117 machine learning methods, we developed the SRGS, which demonstrated robust predictive capability across mult

SUBMITTER: Lin R 

PROVIDER: S-EPMC12858972 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature