Mining phase separation-related diagnostic biomarkers for endometriosis through WGCNA and multiple machine learning techniques: a retrospective and nomogram study.

ABSTRACT:

Objective

The objective of this study was to investigate the role of phase separation-related genes in the development of endometriosis (EMs) and to identify potential characteristic genes associated with the condition.

Methods

We used GEO database data, including 74 non-endometriosis and 74 varying-degree EMs patients. Our approach involved identifying significant gene modules, exploring gene intersections, identifying core genes, and screening for potential EMs biomarkers using weighted gene co-expression network analysis (WGCNA) and various machine learning approaches. We also performed gene set enrichment analysis (GSEA) to understand relevant pathways. This comprehensive approach helps investigate EMs genetics and potential biomarkers.

Results

Nine genes were ide

SUBMITTER: Liang Q 

PROVIDER: S-EPMC11143086 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature