Bioinformatics and functional experiments reveal that

Background: Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that requires improved clinical markers to increase diagnostic accuracy and provide insight into its pathogenesis. Although some biomarkers are available, new ones need to be discovered to better capture the complex physiology of AF. However, their limitations are still not fully addressed. Bioinformatics and functional studies can help find new clinical markers and improve the understanding of AF, meeting the need for early diagnosis and individualized treatment. Methods: To identify AF-related differentially expressed genes (DEGs), We applied the messenger RNA (mRNA) expression profile retrieved in Series Matrix File format from the GSE143924 microarray dataset obtained from the Gene Expression Omnibus (GEO) database, and then used weighted gene co-expression network analysis (WGCNA) to identify the overlapping genes. These genes were analyzed by enrichment analysis, expression analysis and others to obtain the hub gene. Additionally, the potential signaling pathway of hub gene in AF was explored and verified by functional experiments, like quantitative real-time polymerase chain reaction (qRT-PCR), cell counting kit-8 (CCK-8), flow cytometry, and Western blotting (WB) assay. Results: From the GSE143924 data (410 DEGs) and tan module (57 genes), 10 overlapping genes were identified. A central down-regulated gene in AF, Conclusions: While meeting the need for new biomarkers in the diagnosis and prognosis of AF, this study reveals the inherent limitations of current biomarkers. We identified