Bioinformatics and In-Silico Findings Reveal Candidate Genes for Tetralogy of Fallot via Integrative Multi-Omics Data

Background: Tetralogy of Fallot (TOF), the predominant cyanotic congenital heart defect, arises from multifactorial gene-environment interactions disrupting cardiac developmental networks. This study investigated TOF-specific transcriptional alterations and identified high-confidence candidate genes. Methods: Based on GSE36761 transcriptome data, a weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network were conducted to identify TOF-related sub-network and Hub genes. The potential biological functions among these genes were revealed by enrichment analysis. Genetic, epigenetic and transcriptional alteration in the Hub genes were analyzed with leveraged public resources: a methylation dataset (GSE62629) and two single-cell datasets (EGAS00001003996 and GSE126128). Results: Eight Hub genes were identified using the WGCNA network and PPI network, and functional enrichment analysis revealed that GJA1, RUNX2, PTK7, PRICKLE1, and SFRP1 were involved in the morphogenesis of an epithelium, and dysregulation of the signaling were also found in the other two TOF datasets. Furthermore, the study found that the promoters of GJA1, RUNX2, PTK7, and PRICKLE1 genes were hypermethylated and that GJA1 and SFRP1 are highly expressed in mouse second heart field cells and neural crest cells, and the latter is expressed in human embryonic outflow tract cells. Since RUNX2 was not expressed in human and mouse embryonic hearts, GJA1, PTK7, PRICKLE1, and SFRP1 were ultimately identified as TOF candidate genes. Conclusion: Based on the WGCNA network and various bioinformatics analysis approaches, we screened 4 TOF candidate pathogenic genes, and found that the signaling pathways related to the morphogenesis of an epithelium may be involved in the pathogenesis of TOF.