Scholarlink

Background <p>Aortic dissection (AD) is a cardiovascular emergency with high mortality and poor prognosis. This study aimed to identify hub genes associated AD and to assess their predictive efficacy in AD occurrence and their potential biological roles.</p> Methods <p>Gene microarray data were obtained from the Gene Expression Omnibus database. The mRNA data of ascending aortic tissues from AD and control groups were analyzed for differentially expressed genes (DEGs) by three methods: DESeq2, limma and edgeR. Additionally, weighted gene co-expression network analysis (WGCNA) was performed, and the intersection genes identified by the former methods were considered candidate hub genes for AD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods were utilized to explore the potential biological functions of these candidate hub genes. The top 10 hub genes and significant co-expression genes were identified using a protein-protein interaction network and Cytoscape software. Furthermore, the potential causal relationship between the most significant hub gene and AD was analyzed by Mendelian randomization method.</p> Results <p>A total of 1162 DEGs for AD were identified using DESeq2, limma, and edgeR methods. WGCNA clustered AD-related genes into 15 modules. The most relevant modules of AD were intersected with DEGs, resulting in the identification of 201 candidate hub genes. GO and KEGG analyses revealed that these genes were primarily associated with hypoxia, inflammation, cell death, and extracellular matrix (ECM) regulation. The top 10 hub genes for AD were identified as PAI-1, MMP14, ITGA5, CCL2, ITGB3, THBS2, COL18A1, CD68, NFKBIA, and PLAUR. PAI-1 was significantly co-expressed with MMP14, ITGA5, CCL2, and THBS2 in AD. A significant positive correlation between PAI-1 and AD risk was found using inverse variance weighted method (OR = 1.086; 95% CI = 1.006–1.173; <i>P</i> = 0.034).</p> Conclusions <p>AD patients exhibit significant alterations in gene expression. PAI-1 might serve as a potential biomarker and therapeutic target for AD by regulating processes such as hypoxia, inflammation, cell death, and degradation of the ECM.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification of hub genes in aortic dissection based on weighted gene co-expression network analysis and Mendelian randomization study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月1日

查看全文
  • Lei Wang,
  • Qingtong Wu,
  • Yuzuo Lin,
  • Ziyan Lin,
  • Guodong Zhong,
  • Liangwan Chen

摘要

Background

Aortic dissection (AD) is a cardiovascular emergency with high mortality and poor prognosis. This study aimed to identify hub genes associated AD and to assess their predictive efficacy in AD occurrence and their potential biological roles.

Methods

Gene microarray data were obtained from the Gene Expression Omnibus database. The mRNA data of ascending aortic tissues from AD and control groups were analyzed for differentially expressed genes (DEGs) by three methods: DESeq2, limma and edgeR. Additionally, weighted gene co-expression network analysis (WGCNA) was performed, and the intersection genes identified by the former methods were considered candidate hub genes for AD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods were utilized to explore the potential biological functions of these candidate hub genes. The top 10 hub genes and significant co-expression genes were identified using a protein-protein interaction network and Cytoscape software. Furthermore, the potential causal relationship between the most significant hub gene and AD was analyzed by Mendelian randomization method.

Results

A total of 1162 DEGs for AD were identified using DESeq2, limma, and edgeR methods. WGCNA clustered AD-related genes into 15 modules. The most relevant modules of AD were intersected with DEGs, resulting in the identification of 201 candidate hub genes. GO and KEGG analyses revealed that these genes were primarily associated with hypoxia, inflammation, cell death, and extracellular matrix (ECM) regulation. The top 10 hub genes for AD were identified as PAI-1, MMP14, ITGA5, CCL2, ITGB3, THBS2, COL18A1, CD68, NFKBIA, and PLAUR. PAI-1 was significantly co-expressed with MMP14, ITGA5, CCL2, and THBS2 in AD. A significant positive correlation between PAI-1 and AD risk was found using inverse variance weighted method (OR = 1.086; 95% CI = 1.006–1.173; P = 0.034).

Conclusions

AD patients exhibit significant alterations in gene expression. PAI-1 might serve as a potential biomarker and therapeutic target for AD by regulating processes such as hypoxia, inflammation, cell death, and degradation of the ECM.