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Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery
Jing Liu1, Ling Jing2,3, Xilin Tu4
1Department of Cardiology, Harbin the second hospital, Harbin, Heilongjiang, 150056, China. JingLiujll@163.com.
Background:
The analysis of the potential molecule targets of coronary artery disease (CAD) is critical for understanding the molecular mechanisms of disease. However, studies of global microarray gene co-expression analysis of CAD still remain limited.
Methods:
Microarray data of CAD (GSE23561) were downloaded from Gene Expression Omnibus, including peripheral blood samples from CAD patients (n = 6) and controls (n = 9). Limma package in R was used to identify the differentially expressed genes (DEGs) between CAD and control samples. Using weighted gene co-expression network analysis (WGCNA) package in R, WGCNA was performed to identify significant modules in the network. Then, functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID software. Moreover, hub genes in the module were analyzed by isubpathwayminer package in R and GenCLiP 2.0 tool to identify the significant sub-pathways.
Results:
Total 3711 DEGs and 21 modules for them were identified in CAD samples. The most significant module was associated with the pathways of hypertrophic cardiomyopathy and membrane related functions. In addition, the top 30 hub genes with high connectivity in the module were selected, and two genes (G6PD and S100A7) were taken as key molecules via sub-pathway screening and data mining.
Conclusions:
A module associated with hypertrophic cardiomyopathy pathway was detected in CAD samples. G6PD and S100A7 were the potential targets in CAD. Our finding might provide novel insight into the underlying molecular mechanism of CAD.
Insights
This study identified key molecular targets, G6PD and S100A7, for coronary artery disease (CAD) by analyzing gene expression data. These findings offer new insights into the molecular mechanisms driving CAD.
Area of Science:
- Genomics
- Molecular Biology
- Cardiovascular Research
Background:
- Understanding coronary artery disease (CAD) molecular mechanisms requires identifying potential molecule targets.
- Global microarray gene co-expression analysis in CAD remains underexplored.
Purpose of the Study:
- To identify novel molecular targets for coronary artery disease (CAD) using gene co-expression network analysis.
- To elucidate the underlying molecular mechanisms of CAD through pathway and hub gene analysis.
Main Methods:
- Downloaded and analyzed CAD microarray data (GSE23561) from Gene Expression Omnibus.
- Utilized Limma for differentially expressed genes (DEGs) and WGCNA for significant gene modules.
- Performed functional enrichment, pathway analysis, and hub gene identification using DAVID, isubpathwayminer, and GenCLiP 2.0.
Main Results:
- Identified 3711 DEGs and 21 significant gene modules in CAD samples.
- The most significant module was linked to hypertrophic cardiomyopathy and membrane-related functions.
- Selected top 30 hub genes, identifying G6PD and S100A7 as key molecules through sub-pathway analysis.
Conclusions:
- A gene module associated with the hypertrophic cardiomyopathy pathway was identified in CAD.
- G6PD and S100A7 emerged as potential therapeutic targets for coronary artery disease.
- These findings provide novel insights into the molecular mechanisms of CAD.
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