Abstract
Background Aortic dissection (AD) is a lethal vascular disease with high mortality and morbidity. Though AD clinical pathology is well understood, its molecular mechanisms remain unclear. Specifically, gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis. Method Nine patients with AD and 10 healthy controls were enrolled. The gene expression in peripheral mononuclear cells was profiled through next-generation RNA sequencing. Analyses including differential expressed gene (DEG) via DEGseq, weighted gene coexpression network (WGCNA), and VisANT were performed to identify crucial genes associated with AD. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was also utilized to analyze Gene Ontology (GO). Results DEG analysis revealed that 1,113 genes were associated with AD. Of these, 812 genes were markedly reduced, whereas 301 genes were highly expressed, in AD patients. DEGs were rich in certain categories such as MHC class II receptor activity, MHC class II protein complex, and immune response genes. Gene coexpression networks via WGCNA identified 3 gene hub modules, with one positively and 2 negatively correlated with AD, respectively. Specifically, module 37 was the most strongly positively correlated with AD with a correlation coefficient of 0.72. Within module 37, five hub genes (AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D) displayed high connectivity and may have clinical significance in the pathogenesis of AD. Conclusion Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection.
Highlights
Aortic dissection (AD) is a lethal vascular disease characterized by the separation of intima from media and the formation of a false lumen, causing pulsatile blood flow into the aortic wall [1, 2]
Gene expression of AD and health control patients was categorized into two distinct clusters: AD6 and CTLR4 as group one and the rest as group two, with the exception of the outlier of AD9 (Figure 1(a))
A similar result was attained by principal component analysis (PCA) (Figure 1(b)), confirming the differential expression of genes between AD and healthy control patients as two different gene clusters
Summary
Aortic dissection (AD) is a lethal vascular disease characterized by the separation of intima from media and the formation of a false lumen, causing pulsatile blood flow into the aortic wall [1, 2]. Bioinformatics is an effective tool for studying gene expression profiles and revealing potential molecular biological mechanisms. Transcriptomics data from RNA sequencing (RNA-seq) was analyzed via coexpression network analysis to identify the functional association of genes with the disease [4]. Gene expression profiling helps illustrate the potential mechanism of aortic dissection in terms of gene regulation and its modification by risk factors. This study was aimed at identifying the genes and molecular mechanisms in aortic dissection through bioinformatics analysis. Our analysis provides the possible association of specific genes and gene modules for the involvement of the immune system in aortic dissection. AGFG1, MCEMP1, IRAK3, KCNE1, and CLEC4D in module M37 were highly connected and strongly linked with AD, suggesting that these genes may help understand the pathogenesis of aortic dissection
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