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Published on: September 20, 2024
Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome
Qiaoqiao Li1,2,3, Xueping Gao4, Xueshan Luo1,2,3
1Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, China.
Insights
Cardioembolic stroke (CS) involves complex molecular mechanisms. This study identified five key genes (MCEMP1, CLEC4D, GPR97, TSPAN14, FPR2) using bioinformatics, offering potential therapeutic targets for this common ischemic stroke type.
Area of Science:
- Genomics
- Bioinformatics
- Immunology
Background:
- Cardioembolic stroke (CS) is a leading cause of death and disability globally.
- The precise molecular mechanisms driving CS remain incompletely understood.
- Identifying novel molecular targets is crucial for developing effective treatments.
Purpose of the Study:
- To elucidate the molecular mechanisms of cardioembolic stroke (CS) through comprehensive bioinformatics analysis.
- To identify potential diagnostic biomarkers and therapeutic targets for CS.
- To gain new insights into the pathophysiology of CS.
Main Methods:
- Downloaded and analyzed public gene expression datasets (GSE58294, GSE16561).
- Identified differentially expressed genes (DEGs) using the limma package.
- Employed CIBERSORT for immune cell proportion estimation.
- Utilized weighted gene correlation network analysis (WGCNA) and protein-protein interaction (PPI) network analysis to identify hub genes.
- Validated findings using an independent dataset.
Main Results:
- Identified 319 DEGs and clustered 5413 genes into nine modules via WGCNA.
- The 'blue module' showed the highest correlation with stroke, neutrophils, and naive B cells.
- Five hub genes (MCEMP1, CLEC4D, GPR97, TSPAN14, FPR2) were identified through integrated analysis.
- Hub genes demonstrated associations with immune cell infiltration and potential clinical significance.
Conclusions:
- Integrative bioinformatics analysis identified five crucial genes potentially involved in CS pathophysiology.
- These identified genes represent promising targets for future pharmaceutical interventions in CS.
- Further research is warranted to validate these findings and explore therapeutic applications.
Abstract:
Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.
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