Thrombomodulin as a potential diagnostic marker of acute myocardial infarction and correlation with immune infiltration: Comprehensive analysis based on multiple machine learning.

Acute myocardial infarction (AMI) is a global health problem with high mortality. Early diagnosis can prevent the development of AMI and provide valuable information for subsequent treatment. Angiogenesis has been shown to be a critical factor in the development of infarction and targeting this process may be a potential protective strategy for preventing myocardial injury and improving the prognosis of AMI patients. This study aimed to screen and verify diagnostic markers related to angiogenesis in AMI and to investigate the molecular mechanisms of action associated with AMI in terms of immune cell infiltration. The GSE66360 and the GSE60993 datasets were both downloaded from the GEO database and were used as the training cohort and the external validation cohort, respectively. Angiogenesis-related genes (ARGs) were downloaded from the MSigDB database. The hub ARGs were identified via LASSO, RF, and SVM-RFE algorithms. ROC curves were used to assess the accuracy of the hub ARGs. The potential mechanisms of the hub ARGs were analyzed by GSEA. The ssGSEA algorithm was used to determine differences in immune cell infiltration and immune function. The CIBERSORT algorithm was used for immune cell infiltration analysis. In addition, we constructed a ceRNA network map of differentially expressed ARGs. We identified the thrombomodulin (THBD) gene from ARGs as a potential diagnostic marker for AMI based on the LASSO, SVM-RFE, and RF algorithms. THBD was differentially expressed and had a potential diagnostic value (area under the curve [AUC] = 0.931 and 0.765 in the training and testing datasets, respectively). GSEA showed that the MAPK signaling pathway was more enriched in the high-expression group of THBD (P < 0.05). Immune cell infiltration analysis demonstrated that THBD was mainly positively correlated with monocytes (R = 0.48, P = 0.00055) and neutrophils (R = 0.36, P = 0.013). Finally, in the ceRNA regulatory network, THBD was closely associated with 9 miRNAs and 42 lncRNAs involved in AMI. THBD can be used as a potential diagnostic marker for AMI. This study provides new insights for future AMI diagnosis and molecular mechanism research. Moreover, immune cell infiltration plays an essential role in the occurrence and development of AMI.

Liu G ，Huang L ，Lv X ，Guan Y ，Li L ... -  《-》

Identification of potential biomarkers and immune-related pathways related to immune infiltration in patients with acute myocardial infarction.

Acute myocardial infarction (AMI), a medical condition caused by the ischemic necrosis of cardiac tissues, is due to sudden occlusion of the coronary arteries in patients including transplant recipients. It is the leading reason for death and disability worldwide. This study aimed to search potential biomarkers related to the progression of AMI and identify the related immune-related pathways, as also examine their association with the immune cell infiltration and diagnostic value for AMI. Datasets of gene microarray were extracted from (www.ncbi.nlm.nih.gov/geo) the Gene Expression Omnibus (GEO) database and AMI-related biomarkers were obtained by differential expression analysis and weighted correlation network analysis (WGCNA). Subsequently, the support vector machine-recursive feature elimination (SVM-RFE) and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to mine AIM-related hub markers. For the assessment of the diagnostic value of these markers for AMI, the receiver operator characteristic (ROC) curves were plotted. Additionally, the single-sample gene set enrichment analysis (ssGSEA) was performed to determine the immune cell infiltration. A total of 1273 differentially expressed genes (DEGs) were obtained. Nine co-expression modules were obtained after WGCNA. Among them, the brown-colored module was identified as the hub for AMI (correlation [cor] = 0.73, P = 1.1e-87), and intersected with the DEGs yielded a total of 88 shared genes. Subsequently, five hub genes were obtained from the analysis of the LASSO regression and SVM-RFE algorithm. Ultimately, using the ROC curves, the diagnostic values of these genes for AMI were confirmed. The five hub genes were also found to be significantly associated with the infiltration levels of multiple immune cells. Moreover, the DEGs were mainly enriched in the inflammatory and immune-related gene sets evidenced by the functional enrichment analysis. The five hub genes may serve as potential markers for AMI diagnosis and the findings have implications for further investigations on the molecular mechanisms underlying AMI.

Lin Z ，Xu H ，Chen Y ，Zhang X ，Yang J ... -  《-》

Predicting Diagnostic Gene Biomarkers Associated With Immune Infiltration in Patients With Acute Myocardial Infarction.

Objective: The present study was designed to identify potential diagnostic markers for acute myocardial infarction (AMI) and determine the significance of immune cell infiltration in this pathology. Methods: Two publicly available gene expression profiles (GSE66360 and GSE48060 datasets) from human AMI and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 80 AMI and 71 control samples. The LASSO regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were performed to identify candidate biomarkers. The area under the receiver operating characteristic curve (AUC) value was obtained and used to evaluate discriminatory ability. The expression level and diagnostic value of the biomarkers in AMI were further validated in the GSE60993 dataset (17 AMI patients and 7 controls). The compositional patterns of the 22 types of immune cell fraction in AMI were estimated based on the merged cohorts using CIBERSORT. Results: A total of 27 genes were identified. The identified DEGs were mainly involved in carbohydrate binding, Kawasaki disease, atherosclerosis, and arteriosclerotic cardiovascular disease. Gene sets related to atherosclerosis signaling, primary immunodeficiency, IL-17, and TNF signaling pathways were differentially activated in AMI compared with the control. IL1R2, IRAK3, and THBD were identified as diagnostic markers of AMI (AUC = 0.877) and validated in the GSE60993 dataset (AUC = 0.941). Immune cell infiltration analysis revealed that IL1R2, IRAK3, and THBD were correlated with M2 macrophages, neutrophils, monocytes, CD4+ resting memory T cells, activated natural killer (NK) cells, and gamma delta T cells. Conclusion: IL1R2, IRAK3, and THBD can be used as diagnostic markers of AMI, and can provide new insights for future studies on the occurrence and the molecular mechanisms of AMI.

Zhao E ，Xie H ，Zhang Y 《Frontiers in Cardiovascular Medicine》

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration. Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs. A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting. We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Zhang X ，Zhang L ，Gao Y ，Liu Z ，Gong K ... -  《BMC Cardiovascular Disorders》

Identification of feature autophagy-related genes in patients with acute myocardial infarction based on bioinformatics analyses.

To identify feature autophagy-related genes (ARGs) in patients with acute myocardial infarction (AMI) and further investigate their value in the diagnosis of AMI. Gene microarray expression data of AMI peripheral blood samples were downloaded from the GSE66360 dataset. The data were randomly classified into a discovery cohort (21 AMI patients and 22 healthy controls) and a validation cohort (28 AMI patients and 28 healthy controls). Differentially expressed ARGs between patients with AMI and healthy controls in the discovery cohort were identified using a statistical software package. Feature ARGs were screened based on support vector machine-recursive feature elimination (SVM-RFE), and an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value of the classifier, which was further verified in an independent external cohort. A total of seven genes were identified based on SVM-RFE. The SVM classifier had an excellent discrimination ability in both the discovery cohort (area under the curve [AUC] = 0.968) and the validation cohort (AUC = 0.992), which was further confirmed in the GSE48060 dataset (AUC = 0.963). Furthermore, the SVM classifier showed outstanding discrimination between AMI patients with and without recurrent events in the independent external cohort (AUC = 0.992). The identified genes are mainly involved in the cellular response to autophagy, macroautophagy, apoptosis, and the FoxO signaling pathway. Our study identified feature ARGs and indicated their potential roles in AMI diagnosis to improve our understanding of the molecular mechanism underlying the occurrence of AMI.

Du Y ，Zhao E ，Zhang Y 《-》