Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer.

ABSTRACT:

Background

It has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities.

Methods

In this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments

SUBMITTER: Li Q 

PROVIDER: S-EPMC11407950 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature