LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy

Abstract Background: It is well known that atrial fibrillation (AF) is a serious complication of dilated cardiomyopathy (DCM) and they could affect each other. This study endeavors to uncover the shared diagnostic markers and immune pathways contributing to both disorders. Methods: We downloaded AF and DCM data from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to clarify shared signaling pathways of DEGs. Further, the least absolute shrinkage and selection operator (LASSO) regression was applied to pinpoint key hub genes and construct a diagnostic model. Results: A total of 20 common DEGs in AF and DCM were identified for subsequent analyses. The LASSO analysis results showed that RGCC, KCNN2 and TGFBR3 are 3 most important cross-talk genes between these 2 diseases. The immune infiltration results revealed that Th17/Treg cells may play an important role in the pathogeneses of AF and DCM. Conclusion: This pioneering study merges bioinformatics and machine learning to map the shared molecular landscape of AF and DCM, highlighting RGCC, KCNN2, and TGFBR3 as key diagnostic biomarkers. The imbalance of Th17/Treg may be vital in the progression of these 2 diseases.