Integrated bioinformatics analysis and machine learning identifies FZD4, SRPX2, and COL8A1 as angiogenesis hub genes in endometriosis.

ABSTRACT: This study aims to identify angiogenesis-associated genes (AAGs) in endometriosis (EM) by integrating bioinformatics analysis with machine learning, and to investigate their underlying mechanisms. Differentially expressed genes (DEGs) were screened from integrated EM-related gene sets in the Gene Expression Omnibus database. These DEGs were integrated with AAGs retrieved from the AMIGO2 database. Weighted gene co-expression network analysis (WGCNA) was then employed to identify potential EM-AAGs, followed by functional enrichment analysis using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes. Five machine learning algorithms - Random Forest, LASSO, XGBoost, Gradient Boosting Machine (GBM), and SVM-RFE - were utilized for cross-validated screening of hub genes. The diagnostic

SUBMITTER: Li J 

PROVIDER: S-EPMC12558289 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature