Transcriptome data analysis for identifying relevant genes in patients with atrial fibrillation
Li Shuo
Ping Zheng
Niu Huiru
Yuan Yuqing
Cao Xuebin
Abstract：Objective To screen out the key pathogenic genes in atrial fibrillation(AF)through bioinformatics analysis of patient expression profiles.Methods AF datasets GSE79768 and GSE41177 were selected from the GEO database.Expression data from GSE79768 were extracted using R language and subjected to t-test to identify differentially expressed genes(DEGs)with the criteria of P＜0.05 and|log2FC|＞0.585.To identify disease-associated genes,Weighted Gene Co-expression Network Analysis(WGCNA)and LASSO regression were utilized.Subsequently,Gene Ontology(GO)analysis and semantic similarity assessment of these genes were performed.TheSingle-cell Subset GSEA(ssGSEA)method was applied to study immune cell infiltration in atrial fibrillation(AF),followed by a Pearson correlation analysis to examine the relationship between the immune cells and the key genes.Results Five potential diagnostic key genes were identified as LRRC39,LBH,RGS18,WIF1,and EIF5A.LRRC39,LBH,and RGS18 were positively correlated with the infiltration degree of neutrophils and participated in the NOTCH signaling pathway.Conclusion LRRC39,LBH,RGS18,WIF1,and EIF5A are significantly associated with mast cells,neutrophils,and NK cells,and are closely related to the occurrence of atrial fibrillation.They may serve as potential diagnostic genes for atrial fibrillation and provide a basis for clinical diagnosis.
Keywords：Atrial fibrillationBioinformaticsImmune infiltrationDifferentially expressed genes
Publication Date：2025-07-20
Online Publishing Date：2025-09-09(First online date of this platform, not the publication date of the document)
Pages：7( 774-780 )