m6A-related gene clustering analysis and immune cell infiltration analysis in myocardial ischemia-reperfusion injury after cardiopulmonary bypass based on machine learning
- VernacularTitle:基于机器学习的体外循环后心肌缺血-再灌注损伤中m6A相关基因聚类分析和免疫浸润分析
- Author:
Yao TANG
1
;
Wendong CHEN
1
;
Yanqiong WANG
1
;
Wei YANG
1
Author Information
1. Department of Anesthesiology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, P. R. China
- Publication Type:Journal Article
- Keywords:
Cardiopulmonary bypass;
N6-methyladenosine (m6A) RNA methylation;
myocardial ischemia-reperfusion injury;
machine learning
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(10):1475-1485
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify the N6-methyladenosine (m6A)-related characteristic genes analyzed by gene clustering and immune cell infiltration in myocardial ischemia-reperfusion injury (MI/RI) after cardiopulmonary bypass through machine learning. Methods The differential genes associated with m6A methylation were screened by the dataset GSE132176 in GEO, the samples of the dataset were clustered based on the differential gene expression profile, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differential genes of the m6A cluster after clustering were performed to determine the gene function of the m6A cluster. R software was used to determine the better models in machine learning of support vector machine (SVM) model and random forest (RF) model, which were used to screen m6A-related characteristic genes in MI/RI, and construct characteristic gene nomogram to predict the incidence of disease. R software was used to analyze the correlation between characteristic genes and immune cells, and the online website was used to build a characteristic gene regulatory network. Results In this dataset, a total of 5 m6A-related differential genes were screened, and the gene expression profiles were divided into two clusters for cluster analysis. The enrichment analysis of m6A clusters showed that these genes were mainly involved in regulating monocytes differentiation, response to lipopolysaccharides, response to bacteria-derived molecules, cellular response to decreased oxygen levels, DNA transcription factor binding, DNA-binding transcription activator activity, RNA polymerase Ⅱ specificity, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, tumor necrosis factor signaling pathway, interleukin-17 signaling pathway. The RF model was determined by R software as the better model, which determined that METTL3, YTHDF1, RBM15B and METTL14 were characteristic genes of MI/RI, and mast cells, type 1 helper lymphocytes (Th1), type 17 helper lymphocytes (Th17), and macrophages were found to be associated with MI/RI after cardiopulmonary bypass in immune cell infiltration. Conclusion The four characteristic genes METTL3, YTHDF1, RBM15B and METTL14 are obtained by machine learning, while cluster analysis and immune cell infiltration analysis can better reveal the pathophysiological process of MI/RI.
