Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

ABSTRACT:

Background

Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data.

Methods

A comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector

SUBMITTER: Wei C 

PROVIDER: S-EPMC11236550 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature