昆明医科大学学报2025，Vol.46Issue(5)：75-88,14.DOI:10.12259/j.issn.2095-610X.S20250509
机器学习预测IAV和SARS-CoV-2感染引起心脏并发症中的共同靶点基因及H1N1感染模型验证
Prediction of Shared Target Genes in Cardiac Complications Induced by IAV and SARS-CoV-2 Using Machine Learning and Validation in H1N1 Infection Models
摘要
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.关键词
甲型流感病毒/严重急性呼吸综合征冠状病毒2型/心脏病/机器学习Key words
Influenza A virus/SARS-CoV-2/Heart disease/Machine learning分类
基础医学引用本文复制引用
廖元盛,李恒,廖芸,胡云光,殷安国,孔美君,刘龙丁,张莹..机器学习预测IAV和SARS-CoV-2感染引起心脏并发症中的共同靶点基因及H1N1感染模型验证[J].昆明医科大学学报,2025,46(5):75-88,14.基金项目
云南省科技计划资助项目(202202AA100001 （）
202201AT070239 （）
202305AD160006) （）
