In-depth computational analysis reveals the significant dysregulation of key gap junction proteins (GJPs) driving...

Thoracic aortic aneurysm (TAA) represents an aortic pathology that is caused by the deranged integrity of the three layers of the aortic wall and is related to severe morbidity and mortality. Consequently, it is crucial to identify the biomarkers implicated in the pathogenesis and biology of TAA. The aim of the current computational study was to assess the differential gene expression profile of the gap junction proteins (GJPs) in patients with TAA to identify novel potential biomarkers for the diagnosis and treatment of this disease.

We implemented bioinformatics methodology to construct the gene network of the GJPs family, evaluate their expression in pathologic aortic tissue excised from patients with TAA, and compare it with healthy controls. We also investigated the related biological functions and miRNA families.

We extracted raw data related to the transcriptomic profile of selected genes from a microarray dataset, incorporating 43 TAA and 43 healthy control samples. A total of 17 GJPs were evaluated. Eight GJPs (47%) were downregulated in TAA (GJA3, GJA9, GJA10, GJB1 GJC2, GJD2, GJD3, and GJD4). We also demonstrated the important correlations among the differentially expressed genes (DEGs). Four GJPs (GJA3, GJA9, GJC2, and GJD3) were associated with fair discrimination and calibration traits in predicting TAA presentation. Finally, we performed gene set enrichment analysis (GSEA) and identified the major biological functions and miRNA families (hsa-miR-5001-3p, hsa-miR-942-5p, hsa-miR-7113-3p, hsa-miR-6867-3p, and hsa-miR-4685-3p) associated with the DEGs.

These outcomes support the important role of certain gap junction proteins in the pathogenesis of TAA.