Identification and validation of potential biomarkers for atrial fibrillation based on integrated bioinformatics analysis.

ABSTRACT: Background: Globally, the most common form of arrhythmias is atrial fibrillation (AF), which causes severe morbidity, mortality, and socioeconomic burden. The application of machine learning algorithms in combination with weighted gene co-expression network analysis (WGCNA) can be used to screen genes, therefore, we aimed to screen for potential biomarkers associated with AF development using this integrated bioinformatics approach. Methods: On the basis of the AF endocardium gene expression profiles GSE79768 and GSE115574 from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between AF and sinus rhythm samples were identified. DEGs enrichment analysis and transcription factor screening were then performed. Hub genes for AF were screened using WGCNA

SUBMITTER: Tong F 

PROVIDER: S-EPMC10808641 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature