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

OBJECTIVE
Atrial fibrillation (AF) is the most common type of tachycardia. The major injury caused by AF is a systemic embolism. Although AF therapies have evolved substantially, the success rate of sinus rhythm maintenance is relatively low. The reason is the incomplete understanding of the AF mechanisms.


MATERIALS AND METHODS
A Gene Expression Omnibus (GEO) dataset (GSE79768) was downloaded. Differentially expressed genes (DEGs) were identified by bioinformatic analysis. Enriched terms and pathways were identified by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed to determine regulatory genes. CytoHubba and the Molecular Complex Detection (MCODE) algorithm were used to identify potential hub genes and important modules. The Predicting Associated Transcription Factors From Annotated Affinities (PASTAA) method was used to predict transcription factors (TFs).


RESULTS
Two hundred thirty-five upregulated DEGs and seventy-seven downregulated DEGs were identified. In the GO biological process, cellular component, and molecular function analyses, positive regulation of cell migration, anchoring junctions, and cell adhesion molecule binding were enriched significantly. The Hippo signalling 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. Ten hub genes were extracted, including 6 upregulated genes and 4 downregulated genes. CXCR2, TLR4, and CXCR4 may play critical roles in AF. In the TF prediction, we found that Irf-1 may be implicated in AF.


CONCLUSIONS
We found that the CXCR4, TLR4, CXCR2 genes, the Hippo signalling pathway and the class A/1 (rhodopsin-like receptors) module may play critical roles in AF occurrence and maintenance, which may provide novel targets for AF treatment.