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Published on: August 9, 2024
Coronary artery disease associated specific modules and feature genes revealed by integrative methods of WGCNA,
1Department of Internal Medicine-Cardiovascular, Rizhao People's Hospital, Rizhao, Shandong 276826, China.
Insights
This study identified key gene modules and differentially expressed genes (DEGs) linked to coronary artery disease (CAD). These findings offer new insights into the molecular mechanisms driving CAD progression.
Area of Science:
- Genomics
- Molecular Biology
- Cardiovascular Research
Background:
- Coronary artery disease (CAD) is a leading global cause of death.
- Understanding the molecular basis of CAD is crucial for developing effective treatments.
Purpose of the Study:
- To identify specific gene modules associated with CAD.
- To pinpoint feature genes involved in CAD pathogenesis.
Main Methods:
- Utilized WGCNA to detect preserved gene modules across three datasets.
- Employed metaDE for consistent identification of differentially expressed genes (DEGs).
- Constructed a protein-protein interaction (PPI) network and applied machine learning for feature gene analysis.
Main Results:
- Identified nine highly preserved gene modules in CAD.
- Selected 961 DEGs with consistent expression across datasets.
- Constructed a PPI network with 158 overlapping genes, identifying ten key genes in associated KEGG pathways.
Conclusions:
- The study provides novel insights into the molecular mechanisms of CAD.
- Identified potential diagnostic or therapeutic targets for CAD.
Purpose:
Coronary artery disease (CAD) is one of the most common causes of morbidity and mortality globally. This work aimed to investigate the specific modules and feature genes associated with CAD.
Methods:
Three microarray datasets were downloaded from the Gene Expression Omnibus database, which included CAD and healthy samples. WGCNA was applied to identify highly preserved modules across the three datasets. MetaDE method was used to select differentially expressed genes (DEGs) with significant consistency. Protein-protein interaction (PPI) network was constructed using the overlapping genes amongst the DEGs with significant consistency and in the preserved modules. Moreover, a combined machine learning of support vector machine and recursive feature elimination was used to further investigate the feature genes and pathways.
Results:
Nine highly preserved modules were detected in the WGCNA network, and 961 DEGs with significant consistency across the three datasets were selected using the metaDE method. A PPI network was constructed with the 158 overlapping genes. Ten genes were found to be involved in these KEGG pathways directly, including genes CD22, CD79B, CD81, CR1, IKBKE, MAP3K3, MAPK14, MMP9, NCF4, and SPP1.
Conclusions:
The present work might provide novel insight into the underlying molecular mechanism of CAD.
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