Expression pattern of Autophagy-related Genes and Immune Infiltration Landscape in Thoracic Aortic Aneurysm

Abstract

Thoracic aortic aneurysm (TAA), a life-threatening cardiovascular disease, is considered a common cause of death in older individuals. Studies on the molecular mechanism of TAA are very limited. The aim of this study is to explore the expression pattern of autophagy-related genes (ARGs) in TAA, and the related immune infiltration landscape, using bioinformatics analysis. Differentially expressed genes (DEGs) in GSE26155 and differentially expressed miRNAs in GSE110527 were screened by R software. GO annotation and KEGG pathway enrichment analysis were performed on these DEGs. STRING database was employed for the PPI network establishment which was visualized by Cytoscape software. The differentially expressed ARGs were obtained by merging these DEGs with ARGs in autophagy databases HAMDb and HADb. The expression patterns of these differentially expressed ARGs are shown by violin diagram, correlation heatmap, PPI network and enrichment analysis. ROC analysis was used to evaluated the diagnostic value of these differentially expressed ARGs in TAA. Genes with area under the curve (AUC) > 0.7 were considered as TAA biomarkers. TargetScan, miRDB and miRWalk databases were used to predict miRNAs that target these ARGs. Intersection of predicted miRNAs and differentially expressed miRNAs to screen miRNAs that have a clear regulatory relationship with genes. Then a miRNA-mRNA regulatory network was constructed. Following this, the immune infiltration landscape of TAA and the correlation between these ARGs and immunity were further analyzed. In this study, 15 differentially expressed ARGs were screened and the expression patterns of these ARGs were investigated. A miRNA-mRNA regulatory network was constructed. 13 genes with AUC>0.7 in these differentially expressed ARGs were recognized as TAA biomarkers. Finally, the immune infiltration landscape of TAA was shown.