Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models

Yuansheng LIAO; Heng LI; Yun LIAO; Yunguang HU; Anguo YIN; Meijun KONG; Longding LIU; Ying ZHANG

doi:10.12259/j.issn.2095-610X.S20250509

Volume 46 Issue 5

May 2025

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Article Navigation > Journal of Kunming Medical University > 2025 > 46(5): 75-88

Yuansheng LIAO, Heng LI, Yun LIAO, Yunguang HU, Anguo YIN, Meijun KONG, Longding LIU, Ying ZHANG. Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models[J]. Journal of Kunming Medical University, 2025, 46(5): 75-88. doi: 10.12259/j.issn.2095-610X.S20250509

Citation:

Yuansheng LIAO, Heng LI, Yun LIAO, Yunguang HU, Anguo YIN, Meijun KONG, Longding LIU, Ying ZHANG. Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models[J]. Journal of Kunming Medical University, 2025, 46(5): 75-88. doi: 10.12259/j.issn.2095-610X.S20250509

Citation:

Yuansheng LIAO, Heng LI, Yun LIAO, Yunguang HU, Anguo YIN, Meijun KONG, Longding LIU, Ying ZHANG. Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models[J]. Journal of Kunming Medical University, 2025, 46(5): 75-88. doi: 10.12259/j.issn.2095-610X.S20250509

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Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models

doi: 10.12259/j.issn.2095-610X.S20250509

Institute of Medical Biology，Chinese Academy of Medical Sciences & Peking Union Medical College，Kunming Yunnan 650118，China

Received Date: 2025-02-02
Publish Date: 2025-05-30

Abstract

Abstract

Objective To predict and preliminarily validate potential shared key genes involved in cardiac complications caused by influenza A virus （IAV） and severe acute respiratory syndrome coronavirus 2 （SARS-CoV-2） infections. Methods Differentially expressed genes （DEGs） associated with cardiac complications were obtained from the Gene Expression Omnibus （GEO） database. A hierarchical intersection strategy was applied. First, cardiac complication related DEGs were overlapped with 2 independent virus related gene sets: 3 454 human genes linked to IAV infection in GeneCards and 333 human protein-coding genes interacting with SARS-CoV-2 in the Human Protein Atlas. The 2 overlap results were then intersected to yield 22 hub genes. Lasso regression, random forest （RF） and support vector machine algorithms （SVM） were employed to refine this list. Predicted genes were validated in vitro in H1N1-infected human cardiomyocyte AC16 cells and in vivo in IFITM3 knockout mice challenged with H1N1, assessing transcriptional changes. Results A total of 22 hub genes were identified through integrative bioinformatics analysis. Application of the 3 machine learning algorithms resulted in 5 common key genes: ACE2, TBK1, NUP210, PUSL1, and MEPCE. In vitro infection of AC16 cells with H1N1 revealed dynamic transcriptional changes in all 5 genes post-infection （P < 0.05）. In vivo experiments using H1N1-infected IFITM3 knockout mice confirmed the dynamic mRNA expression changes of these 5 genes, consistent with the in vitro results （P < 0.05）. Conclusion By combining multilayered bioinformatics analysis with 3 machine learning approaches, 5 common key genes are identified: ACE2, TBK1, NUP210, PUSL1 and MEPCE. Validation in H1N1 infection models confirms their relevance to IAV-induced cardiac complications.
- Influenza A virus,
- SARS-CoV-2,
- Heart disease,
- Machine learning

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References(46)

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