Identification and verification of FN1, P4HA1 and CREBBP as potential biomarkers in human atrial fibrillation.

Background: Atrial fibrillation (AF) is a common arrhythmia that can lead to cardiac complications. The mechanisms involved in AF remain elusive. We aimed to explore the potential biomarkers and mechanisms underpinning AF.

Methods: An independent dataset, GSE2240, was obtained from the Gene Expression Omnibus database. The R package, "limma", was used to screen for differentially expressed genes (DEGs) in individuals with AF and normal sinus rhythm (SR). Weighted gene co-expression network analysis (WGCNA) was applied to cluster DEGs into different modules based on functional disparities. Enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction network was constructed, and hub genes were identified using cytoHubba. Quantitative reverse-transcription PCR was used to validate mRNA expression in individuals with AF and SR.

Results: We identified 2, 589 DEGs clustered into 10 modules using WGCNA. Gene Ontology analysis showed specific clustered genes significantly enriched in pathways associated with the extracellular matrix and collagen organization. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that the target genes were mainly enriched for proteoglycans in cancer, extracellular matrix-receptor interaction, focal adhesion, and the PI3K-Akt signaling pathway. Three hub genes, FN1, P4HA1 and CREBBP, were identified, which were highly correlated with AF endogenesis. mRNA expression of hub genes in patients with AF were higher than in individuals with normal SR, consistent with the results of bioinformatics analysis.

Conclusions: FN1, P4HA1, and CREBBP may play critical roles in AF. Using bioinformatics, we found that expression of these genes was significantly elevated in patients with AF than in individuals with normal SR. Furthermore, these genes were elevated at core positions in the mRNA interaction network. These genes should be further explored as novel biomarkers and target candidates for AF therapy.