Potential biomarkers of aortic dissection based on expression network analysis

Abstract

Abstract Background Aortic dissection (AD) is a rare disease with severe morbidity and high mortality. Presently, the pathogenesis of aortic dissection is still not completely clear, and studying its pathogenesis will have important clinical significance. Methods We downloaded 28 samples from the Gene Expression Omnibus (GEO) database (Accession numbers: GSE147026 and GSE190635), including 14 aortic dissection samples and 14 healthy controls (HC) samples. The Limma package was used to screen differentially expressed genes. The StarBasev2.0 tool was used to predict the upstream molecular circRNA of the selected miRNAs, and Cytoscape software was used to process the obtained data. STRING database was used to analyze the interacting protein pairs of differentially expressed genes under medium filtration conditions. The R package "org.hs.eg.db" was used for functional enrichment analysis. Results Two hundred genes associated with aortic dissection were screened. Functional enrichment analysis was performed based on these 200 genes. At the same time, 2720 paired miRNAs were predicted based on these 200 genes, among which hsa-miR-650, hsa-miR-625-5p, hsa-miR-491-5p and hsa-miR-760 paired mRNAs were the most. Based on these four miRNAs, 7106 pairs of circRNAs were predicted to be paired with them. The genes most related to these four miRNAs were screened from 200 differentially expressed genes (CDH2, AKT1, WNT5A, ADRB2, GNAI1, GNAI2, HGF, MCAM, DKK2, ISL1). Conclusions The study demonstrates that miRNA-associated circRNA-mRNA networks are altered in AD, implying that miRNA may play a crucial role in regulating the onset and progression of AD. It may become a potential biomarker for the diagnosis and treatment of AD.

Keywords

• Aortic dissection
• miRNA
• mRNA
• GO analysis
• PPI network