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ORIGINAL RESEARCH article

Front. Cardiovasc. Med.
Sec. Cardiovascular Genetics and Systems Medicine
Volume 11 - 2024 | doi: 10.3389/fcvm.2024.1375768

Exploring the Shared Biomarkers between Cardioembolic Stroke and Atrial Fibrillation by WGCNA and Machine Learning

Provisionally accepted
Jingxin Zhang Jingxin Zhang 1Bingbing Zhang Bingbing Zhang 1Tengteng Li Tengteng Li 1Yibo Li Yibo Li 1Qi Zhu Qi Zhu 1Xiting Wang Xiting Wang 2*Tao Lu Tao Lu 1*
  • 1 Beijing University of Chinese Medicine, Beijing, China
  • 2 Chinese Academy of Sciences (CAS), Beijing, Beijing, China

The final, formatted version of the article will be published soon.

    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.In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify five 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.

    Keywords: Cardioembolic stroke, Atrial Fibrillation, bioinformatics, Weighted gene coexpression network analysis, Hub gene

    Received: 24 Jan 2024; Accepted: 09 Aug 2024.

    Copyright: © 2024 Zhang, Zhang, Li, Li, Zhu, Wang and Lu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Xiting Wang, Chinese Academy of Sciences (CAS), Beijing, 100864, Beijing, China
    Tao Lu, Beijing University of Chinese Medicine, Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.