Background: Study have shown that atrial fibrillation (AF) is a disease with genetic risk, and its pathogenesis is still unclear. This study sought to screen the gene microarray data of AF patients and to perform a bioinformatics analysis to identify AF signature diagnostic genes. Methods:The AF gene sets from the Gene Expression Omnibus (GEO) database were screened, and the differentially expressed genes (DEGs) were identified after the normalization of the data set by R software.We conducted a gene set enrichment analysis, a protein-protein interaction (PPI) network analysis, a gene-gene interaction (GGI) network analysis, and an immuno-infiltration analysis. The core genes were identified from the DEGs, and base on receiver operating characteristic, the top 5 core genes in the 2 data sets were selected as diagnostic factors and a nomogram was constructed. The miRNA of the core genes were predicted and an immune cell correlation analysis was performed.Results: A total of 20 DEGs were identified. The functions of these DEGs were mainly related to muscle contraction, autophagosome, and bone morphogenetic protein (BMP) binding, and focused on the calcium signaling pathway, ferroptosis, the extracellular matrix-receptor interaction, and other pathways. A total of 5 core genes [i.e., GPR22 (G protein-coupled receptor 22), COG5 (component of oligomeric golgi complex 5), GALNT16 (polypeptide N-acetylgalactosaminyltransferase 16), OTOGL (otogelin-like), and MCOLN3 (mucolipin 3)] were identified, and a linear model for risk prediction was constructed, which has good prediction ability. Plasma cells and Macrophages M2 were significantly increased in AF, while T cells follicular helper and Dendritic cells activated were significantly decreased.Conclusions: In our study, we identified 5 potential diagnostic key genes (i.e., GPR22, COG5, GALNT16, OTOGL, and MCOLN3). Our findings may provide a theoretical basis for susceptibility analyses and target drug development in AF.

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