ArticleBMC cardiovascular disorders2016
Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease.
Article in BMC cardiovascular disorders, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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53 citing papers in PubMed, 95 citations in OpenAlex.
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Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsMicroarray 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.
resultsTotal 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.
conclusionsA 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.
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