Integration of Gene Expression Profile Data to Verify Hub Genes of Patients with Stanford A Aortic Dissection

Thoracic aortic dissection (TAD) is a catastrophic disease worldwide, but the pathogenic genes and pathways are largely unclear. This study aims at integrating two gene expression profile datasets and verifying hub genes and pathways involved in TAD as well as exploring potential molecular mechanisms. We will combine our mRNAs expression profile (6 TAD tissues versus 6 non-TAD tissues) and GSE52093 downloaded from the Gene Expression Omnibus (GEO) database. The two mRNAs expression profiles contained 13 TAD aortic tissues and 11 non-TAD tissues. The two expression profile datasets were integrated and we found out coexpression of differentially expressed genes (DEGs) using bioinformatics methods. The gene ontology and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes online analyses, respectively. The protein-protein interaction networks of the DEGs were constructed according to the data from the STRING database. Cytohubber calculating result shows the top 10 hub genes with CDC20, AURKA, RFC4, MCM4, TYMS, MCM2, DLGAP5, FANCI, BIRC5, and POLE2. Module analysis revealed that TAD was associated with significant pathways including cell cycle, vascular smooth muscle contraction, and adrenergic signaling in cardiomyocytes. The qRT-PCR result showed that the expression levels of all the hub genes were significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of TAD.