Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction

Frontiers in Microbiology

Microbiology

Ying Liu¹  Ming Xu²  Longbin Wang²  Andras Hajdu³  Xufeng Huang⁴  Shujing Zhou⁴  Zhengrui Li⁵  Ling Zhang⁵ 

[1] Department of Cardiology, Sixth Medical Center, PLA General Hospital, Beijing, China;Department of Clinical Veterinary Medicine, Huazhong Agricultural University, Wuhan, China;Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, Hungary;Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, Hungary;Faculty of Medicine, University of Debrecen, Debrecen, Hungary;Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China;College of Stomatology, Shanghai Jiao Tong University, Shanghai, China;National Center for Stomatology, Shanghai, China;National Clinical Research Center for Oral Diseases, Shanghai, China;Shanghai Key Laboratory of Stomatology, Shanghai, China;Shanghai Research Institute of Stomatology, Shanghai, China;

关键词: COVID-19;    acute myocardial infarction;    diagnostic biomarkers;    machine learning;    causal relationship;    bioinformatics;   

DOI  :  10.3389/fmicb.2023.1153106

 received in 2023-01-28, accepted in 2023-02-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundIncreasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication.Materials and methodsThe gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue.ResultsOverall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified.ConclusionFor the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract

【 授权许可】

Unknown   
Copyright © 2023 Liu, Zhou, Wang, Xu, Huang, Li, Hajdu and Zhang.