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

Abstract

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.

Ovarian Cancer Risk Scores Based on Immune-Related Pseudogenes to Predict Overall Survival and Guide Immunotherapy and Chemotherapy

“…Increasing evidence indicated that the immune system plays a vital role in malignancy initiation, cancer progression, and cancer therapeutic responses. Immune cells have been confirmed to have a significant influence on various disease processes, including cancer progression [21][22][23]. So far, immunotherapies in OC are remaining in the exploratory stages.…”

An Immune Checkpoint-Related Gene Signature for Predicting Survival of Pediatric Acute Myeloid Leukemia

“…The algorithm calculated the supposed immune cells' abundance by using a comparison range of 1000 permutations of 22 immune cell subtypes (LM22) [ 23 ]. In order to determine immune violations of each sample, we used the mRNA expressions matrix as input files [ 24 ]. CIBERSORT production of p < 0.05 was filtered for subsequent study, indicating inferred proportion of CIBERSORT generated immune cell number is precise [ 25 ].…”

Development and External Validation of a Novel Immune Checkpoint–Related Gene Signature for Prediction of Overall Survival in Hepatocellular Carcinoma

“…The algorithm estimated the putative abundance of immune cells using a reference set with 22 immune cell subtypes (LM22) with 1,000 permutations (Newman et al, 2015 ). We used the mRNA expression matrix as the input files to evaluate the immune infractions of each sample through the CIBERSORT algorithm (Zhao et al, 2020 ). Cases with a CIBERSORT output of P < 0.05, demonstrating that the inferred proportions of immune cell populations produced by CIBERSORT are accurate (Ali et al, 2016 ), were filtered out for subsequent analysis.…”