Bioinformatic analysis for the identification of potential gene interactions and therapeutic targets in atrial fibrillation

Abstract

AbstractBackgroundAtrial fibrillation (AF) is the most prevalent tachycardia. The major injuries caused by AF are systemic embolism and heart failure. Although AF therapies have evolved substantially in recent years, the success rate of sinus rhythm maintenance is relatively low. The reason is the incomplete understanding of the AF mechanisms.Material and methodIn this study, profiles were downloaded from the GEO database (GSE79762). Bioinformatic analysis was used to identify differentially expressed genes (DEGs). GO analysis and KEGG analysis were performed to identify the most enriched terms and pathways. A protein-protein interaction network was constructed to determine regulatory genes. Key modules and hub genes were identified by MOCDE and cytoHubba. Transcription factors (TFs) were predicted by PASTAA.ResultsSeventy-seven up-regulated DEGs and 236 downregulated DEGs were identified. In the GO biological process, cellular components, and molecular function analysis, positive regulation of cell migration, anchoring junction and cell adhesion molecule binding were the most significant enrichment terms. The Hippo signaling pathway was the most significantly enriched pathway. In the PPI network analysis, we found that Class A/1 (rhodopsin-like receptors) may be the critical module in AF. Ten hub genes were extracted, including 4 upregulated genes and 6 downregulated genes. CXCR2, TLR4 and CXCR4 may play critical roles in AF. In TF prediction, we found that Irf-1 may be implicated in AF.ConclusionOur study found that the CXCR4, TLR4, CXCR2; Hippo signaling pathway; and class A/1 (rhodopsin-like receptors) modules may play critical roles in AF occurrence and maintenance. This may provide novel targets for AF treatment.