Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.

ABSTRACT:

Background

Hyperthyroidism and atrial fibrillation (AF) are interrelated conditions with significant cardiovascular impact. While their clinical association is established, the molecular mechanisms remain unclear. Identifying shared biomarkers and pathways can advance understanding and guide therapy.

Methods

The hyperthyroidism dataset GSE71956 and the AF dataset GSE115574 were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis was performed using the "limma" package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. To validate the predictive results,

SUBMITTER: Wang L 

PROVIDER: S-EPMC12675458 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature