Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall

Abstract

Background The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall. Methods The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets. Result A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression. Conclusion We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Post-GWAS machine learning prioritizes key genes regulating blood pressure

“…Our gene expression analysis found MYH11 (scored 0.915 by XGB), ACTA2 (scored 0.9 by XGB), and TPM2 (scored 0.865 by XGB) as highly expressed in cardiovascular tissues and have interconnected PPIs. In comparison, studies have found all three of these genes are coexpressed in vascular smooth muscle cells to weaken the aortic wall 43 and to be markers of pulmonary hypertension 44 . We also analysed these clustered genes in DGIdb, nding MYH11 and ACTA2 are both clinically actionable drug targets, suggesting these highly prioritized genes and their interaction network offer targeted directions of investigation with putative drug targets for BP.…”

The mitochondrial protease ClpP is a druggable target that controls VSMC phenotype by a SIRT1-dependent mechanism

“…Furthermore, in animal models of aortic aneurysms, SMC-specific gene downregulation precedes the weakening of the arterial wall [ 69 ] and KLF4 deficiency inhibits aneurysm formation [ 60 ]. Additionally, heterozygous mutations, or differential expression of proteins from the VSMC contractile apparatus, as well as the kinases that regulate their assembly, have been linked to the formation of aneurysms in humans [ 6 , 7 , [10] , [11] , [12] , [13] , [14] , [15] ].…”

“…This ability is critical for vessel maturation, remodeling, and injury repair but also increases susceptibility to vascular diseases [ 1 ]. For instance, in aortic aneurysms, weakening of arterial walls results from complex vessel wall remodeling, where VSMC phenotypic modulation plays a central role [ [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] ].…”

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network

“…In recent decades, genome-wide analyses, such as high-throughput sequencing technology and gene chips, have been routinely used to study gene expression patterns [ 13 ]. The analysis of these data has fostered the development of bioinformatics and derived methods such as Weighted Gene Co-Expression Network Analysis (WGCNA) and Receiver Operating Characteristic (ROC) analysis, which can provide valuable information for biomarker discovery of diseases [ 14 – 16 ]. In this study, we used the gene expression datasets from different populations in different countries to screen out the common differentially expressed genes (DEGs) in peripheral blood cells of patients with or without sepsis.…”