Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer.

ABSTRACT:

Background

Prostate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets.

Methods

Integrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immun

SUBMITTER: Gao F 

PROVIDER: S-EPMC11414577 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature