Comprehensive bioinformatics analysis of hub genes in ischemic heart failure and atrial fibrillation.

Front Cardiovasc Med

Department of Rehabilitation, Huadong Hospital, Fudan University, Shanghai, China.

Published: February 2025

Background: Atrial fibrillation (AF) and heart failure (HF) frequently coexist and mutually influence each other. The association between AF and the subtype of HF, Ischaemic heart failure (IHF), remains insufficiently described, despite their high prevalence. Hence, comprehending their underlying pathophysiological mechanisms and identifying new therapeutic targets are urgently needed.

Objective: This exploration aims to unearth related genes and pathways of IHF and AF, offering new perspectives for their joint diagnosis and treatment.

Methods: Datasets for HF (GSE57338) and AF (GSE128188) were acquired from the Gene Expression Omnibus (GEO) database. Intersecting these sets generated common differentially expressed genes (DEGs) for further analyses, including Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction (PPI), and hub gene identification. Subsequently, the HF dataset (GSE116250) and AF dataset (GSE2240) were utilized to confirm the expression of the hub genes, followed by examination of gene expression patterns across cells in single-cell datasets.

Results: The study identified 20 common DEGs. Among them, 10 hub genes (SFRP4, FMOD, HAPLN1, LTBP2, SVEP1, BCL6, ANPEP, CD38, ATRNL1, and BEX1) were found to be associated with the co-occurrence of IHF and AF. Enrichment analysis revealed the predominant involvement of these hub genes in extracellular matrix (ECM). Data from the Uniprot database revealed the involvement of the Wnt signaling pathway and TGF-β1/Smads signaling pathway in the development and progression of AF and IHF. Single-cell analysis demonstrated high gene expression primarily in monocytes.

Conclusion: The identified 10 hub genes can serve as potentially valuable biomarkers for IHF and AF. Enrichment analysis reveals that these potential biomarkers are significantly associated with ECM, nicotinate, and nicotinamide metabolism, providing a foundational target for the joint diagnosis and treatment of the two diseases.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11891352PMC
http://dx.doi.org/10.3389/fcvm.2025.1499065DOI Listing

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