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

• Published in: Journal of translational medicine 2023-10, Vol.21 (1), p.1-761, Article 761
• Main Authors: Zhang, Lin, Liu, Yue, Wang, Kaiyue, Ou, Xiangqin, Zhou, Jiashun, Zhang, Houliang, Huang, Min, Du, Zhenfang, Qiang, Sheng
• Format: Article
• Language: English
• Subjects: Acute myocardial infarction; Algorithms; AQP9; Cardiovascular disease; Classification; Clinical trials; Coronary artery disease; Cytokines; Data mining; Diagnosis; Diagnostic gene identification; DNA microarrays; Evolution; Gene expression; Genes; Genetic markers; Genetic research; Genetic transcription; Heart attack; Heart attacks; Heart diseases; Immune cell correlation; Learning algorithms; Leukocytes; Leukocytes (neutrophilic); Machine learning; Monocytes; Myocardial infarction; Neural networks; Ontology; Ribonucleic acid; RNA; SOCS3; Support vector machines
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-023-04573-x

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 thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. Conclusion AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value. Keywords: Acute myocardial infar