Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction
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 se...
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| Format: | Article |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1153106/full |
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| author | Ying Liu Shujing Zhou Shujing Zhou Longbin Wang Ming Xu Xufeng Huang Xufeng Huang Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Andras Hajdu Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang |
| author_facet | Ying Liu Shujing Zhou Shujing Zhou Longbin Wang Ming Xu Xufeng Huang Xufeng Huang Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Andras Hajdu Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang |
| author_sort | Ying Liu |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-04-09T21:03:04Z |
| format | Article |
| id | doaj.art-71b83484ed3a48eaa0a338038d405306 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-09T21:03:04Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-71b83484ed3a48eaa0a338038d4053062023-03-29T05:56:06ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-03-011410.3389/fmicb.2023.11531061153106Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarctionYing Liu0Shujing Zhou1Shujing Zhou2Longbin Wang3Ming Xu4Xufeng Huang5Xufeng Huang6Zhengrui Li7Zhengrui Li8Zhengrui Li9Zhengrui Li10Zhengrui Li11Zhengrui Li12Andras Hajdu13Ling Zhang14Ling Zhang15Ling Zhang16Ling Zhang17Ling Zhang18Ling Zhang19Department of Cardiology, Sixth Medical Center, PLA General Hospital, Beijing, ChinaDepartment of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, HungaryFaculty of Medicine, University of Debrecen, Debrecen, HungaryDepartment of Clinical Veterinary Medicine, Huazhong Agricultural University, Wuhan, ChinaDepartment of Clinical Veterinary Medicine, Huazhong Agricultural University, Wuhan, ChinaDepartment of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, HungaryFaculty of Medicine, University of Debrecen, Debrecen, HungaryDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaCollege of Stomatology, Shanghai Jiao Tong University, Shanghai, ChinaNational Center for Stomatology, Shanghai, ChinaNational Clinical Research Center for Oral Diseases, Shanghai, ChinaShanghai Key Laboratory of Stomatology, Shanghai, China0Shanghai Research Institute of Stomatology, Shanghai, ChinaDepartment of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, HungaryDepartment of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaCollege of Stomatology, Shanghai Jiao Tong University, Shanghai, ChinaNational Center for Stomatology, Shanghai, ChinaNational Clinical Research Center for Oral Diseases, Shanghai, ChinaShanghai Key Laboratory of Stomatology, Shanghai, China0Shanghai Research Institute of Stomatology, Shanghai, ChinaBackgroundIncreasing 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 abstracthttps://www.frontiersin.org/articles/10.3389/fmicb.2023.1153106/fullCOVID-19acute myocardial infarctiondiagnostic biomarkersmachine learningcausal relationshipbioinformatics |
| spellingShingle | Ying Liu Shujing Zhou Shujing Zhou Longbin Wang Ming Xu Xufeng Huang Xufeng Huang Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Zhengrui Li Andras Hajdu Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang Ling Zhang Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction Frontiers in Microbiology COVID-19 acute myocardial infarction diagnostic biomarkers machine learning causal relationship bioinformatics |
| title | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
| title_full | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
| title_fullStr | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
| title_full_unstemmed | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
| title_short | Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction |
| title_sort | machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between covid 19 and acute myocardial infarction |
| topic | COVID-19 acute myocardial infarction diagnostic biomarkers machine learning causal relationship bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1153106/full |
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