Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients

作者：Lin Zhang, Yue Liu, Kaiyue Wang, Xiangqin Ou, Jiashun Zhou, Houliang Zhang, Min Huang, Zhenfang Du, Sheng Qiang · 发表于：Journal of Translational Medicine · 年份：2023 · DOI：10.1186/s12967-023-04573-x · 被引用次数：19 · 研究领域：Cardiac Fibrosis and Remodeling、Adipokines, Inflammation, and Metabolic Diseases、Gene expression and cancer classification

BACKGROUND: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. METHODS: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. RESULTS: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the th...