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Published on: July 20, 2022
Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning
Jingxin Zhang1, Bingbing Zhang1, Tengteng Li1
1School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China.
Insights
Bioinformatics and machine learning identified four shared biomarkers (PIK3R1, ITGAM, FOS, TLR4) linking cardioembolic stroke and atrial fibrillation. Immune response pathways are implicated, suggesting new therapeutic targets for these common cardiovascular diseases.
Area of Science:
- Biomedical Informatics
- Cardiovascular Research
- Genomics
Background:
- Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent conditions with significant societal impact.
- The intricate relationship and shared etiological mechanisms between CS and AF remain poorly understood.
- Investigating shared biomarkers is crucial for understanding disease pathogenesis and developing targeted therapies.
Purpose of the Study:
- To identify shared molecular biomarkers between Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) using bioinformatics and machine learning.
- To explore the functional pathways and protein-protein interactions associated with these shared biomarkers.
- To construct a predictive model for CS and AF based on identified biomarkers.
Main Methods:
- Weighted Gene Co-expression Network Analysis (WGCNA) on CS and AF gene expression datasets from GEO.
- Gene Ontology (GO) and KEGG pathway enrichment analysis of shared genes.
- Protein-Protein Interaction (PPI) network construction using STRING to identify hub genes.
- Machine learning models and ROC curve analysis for biomarker validation.
Main Results:
- Functional enrichment analysis highlighted the involvement of immune response pathways in both CS and AF.
- Protein-Protein Interaction network analysis identified four key genes (PIK3R1, ITGAM, FOS, TLR4) associated with both conditions.
- Machine learning models demonstrated the diagnostic potential of these identified biomarkers.
Conclusions:
- Four hub genes (PIK3R1, ITGAM, FOS, TLR4) were identified as significantly associated with both CS and AF.
- The findings suggest that immune response pathways play a critical role in the pathogenesis of CS and AF.
- These identified biomarkers and pathways offer potential avenues for future research into etiological mechanisms and therapeutic strategies.
Background:
Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.
Methods:
We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.
Results:
Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.
Conclusion:
In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.
