Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning

• DOI: 10.3389/fcvm.2024.1375768
• Publication Date: 2024-08-29T05:00:00Z
• Authors: Jingxin Zhang; Bingbing Zhang; Tengteng Li; Yibo Li; Qi Zhu; Xiting Wang; Tao Lu
• Keywords: KEGG; Gene co-expression network

Background Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life impose considerable financial burdens on society. Despite increasing evidence a significant association between two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis employed machine learning techniques to investigate potential shared biomarkers CS AF. Methods retrieved AF datasets from Gene Expression Omnibus (GEO) database applied Weighted Co-Expression Network Analysis (WGCNA) develop co-expression networks aimed at identifying pivotal modules. Next, we performed Ontology (GO) Kyoto Encyclopedia Genes Genomes (KEGG) pathway enrichment genes within modules related The STRING was used build protein-protein interaction (PPI) network, facilitating discovery hub network. Finally, several common approaches were construct clinical predictive model ROC curve evaluate diagnostic value identified for CS. Results Functional indicated pathways intrinsic immune response may be involved in PPI network 4 key with both AF, specifically PIK3R1, ITGAM, FOS, TLR4. Conclusion In our study, utilized WGCNA, analysis, identify four associated annotation outcomes revealed inherent connected recognized might could pave way further research etiological mechanisms therapeutic targets