Weighted gene co‑expression network analysis to identify key modules and hub genes associated with atrial fibrillation

Abstract

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl-CoA Acetyltransferase 1, death domain-containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

[Retracted] Multiomics Analysis of Genetics and Epigenetics Reveals Pathogenesis and Therapeutic Targets for Atrial Fibrillation

“…Intriguingly, we found that PCDHA family genes were all hypermethylated and lowly expressed in AF compared to controls, which might become underlying biomarkers for AF. WGCNA has been widely applied to explore complex biological processes by construction of gene coexpression networks and functional key modules associated with clinical features, which could provide comprehensive insights into specific diseases or conditions [27]. In this study, WGCNA was used to identify potential mechanisms and biomarkers or therapeutic targets for AF using microarray expression profiles.…”

Convergent and distributed effects of the schizophrenia-associated 3q29 deletion on the human neural transcriptome

“…Table S2.3). To generate rigorous WGCNA-based predictions, we adopted a conservative criterion that defines hub genes as nodes with kME > 0.8 (P < 0.05), a stringent threshold used by several other studies [52][53][54]. This analysis revealed that only one 3q29 interval gene is a hub gene: UBXN7 (kME = 0.84, P = 8.33E-30), which encodes a ubiquitin ligase-substrate adaptor [55,56].…”

Identification of key modules and hub genes associated with lung function in idiopathic pulmonary fibrosis

“…WGCNA is a bioinformatic algorithm and has been used to identify candidate biomarkers and therapeutic targets for many diseases, especially in cancer and neuroscience research (Giulietti et al, 2017;Li et al, 2020;Niemira et al, 2019;Rangaraju et al, 2018;Spiers et al, 2015;Zeleznik et al, 2020). We can identify clusters (modules) of highly correlated genes using WGCNA.…”