Identification of endometrial cancer biomarkers using weighted gene coexpression network analysis and machine learning.

ABSTRACT:

Background

Endometrial carcinoma (UCEC) exhibits a rising incidence in China, imposing a substantial burden on both women and society. Identifying biomarkers for UCEC is critical for precise diagnosis and treatment.

Methods

RNA-seq data for UCEC and normal endometrial tissues were sourced from the GEO and TCGA databases. The Limma package was used to analyze differentially expressed genes (DEGs) from GEO datasets, while weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with UCEC. Machine learning algorithms (GBM, KNN, LASSO, SVM, NNET, RF, DT, GLM) were subsequently applied for further gene screening. Key biomarkers were identified by analyzing overall survival (OS) and progression-free survival (PFS) using TCGA-UCEC data o

SUBMITTER: Wang H 

PROVIDER: S-EPMC12911402 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature