Department of Vascular Interventional Surgery, the First Affiliated Hospital of Harbin Medical University, No.23 Youzheng Str, Nangang District, Harbin, 150001, Heilongjiang, China.
Department of Vascular Surgery, Tian Jin First Center Hospital, Tianjin, 300192, China.
Hereditas. 2021 Dec 2;158(1):35. doi: 10.1186/s41065-021-00200-1.
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.
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.
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.
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.
腹主动脉瘤(AAA)的直径是预测 AAA 破裂发生的最常用参数。然而,主动脉壁最脆弱的区域可能与压力下 AAA 最扩张的区域不同。本研究首次使用加权基因共表达网络分析(WGCNA)来检测导致主动脉壁区域性减弱的共表达基因。
本研究使用 GSE165470 原始微阵列数据集。使用“limma”R 包筛选差异表达基因(DEGs)。通过基因本体论生物过程(GO-BP)和京都基因与基因组百科全书(KEGG)分析评估 DEGs。使用 WGCNA 构建 AAA 壁区域性减弱的样本和对照组的共表达网络,以检测基因模块。在显著功能模块中定义枢纽基因,并根据蛋白质-蛋白质相互作用(PPIs)构建具有最高置信度的枢纽差异表达基因(hDEG)共表达网络。使用分子化合物检测(MCODE)识别 hDEG 共表达网络中的关键基因。使用 GSE7084 和 GSE57691 微阵列基因表达数据集验证 hDEG 共表达网络中的关键基因。
共鉴定出 350 个 DEGs,包括 62 个上调和 288 个下调 DEGs。通路涉及免疫反应、血管平滑肌收缩和 DEGs 在 AAA 区域性减弱样本中的细胞-基质粘附。基于与减弱的动脉瘤壁特征负相关的结果,antiquewhite3 是最显著的模块,用于识别下调的 hDEGs。鉴定并验证了 7 个关键基因:ACTG2、CALD1、LMOD1、MYH11、MYL9、MYLK 和 TPM2。这些关键基因与 AAA 进展的机制有关。
我们鉴定出可能在 AAA 壁减弱中起重要作用的关键基因,这些基因可能是医学治疗和诊断生物标志物的潜在靶点。需要进一步研究更全面地阐明关键基因在 AAA 区域性减弱发病机制中的功能。