Identifying patterns of immune related cells and genes in the peripheral blood of acute myocardial infarction patients using a small cohort

Abstract

Background The immune system plays a vital role in the pathophysiology of acute myocardial infarction (AMI). However, the exact immune related mechanism is still unclear. This research study aimed to identify key immune-related genes involved in AMI. Methods CIBERSORT, a deconvolution algorithm, was used to determine the proportions of 22 subsets of immune cells in blood samples. The weighted gene co-expression network analysis (WGCNA) was used to identify key modules that are significantly associated with AMI. Then, CIBERSORT combined with WGCNA were used to identify key immune-modules. The protein–protein interaction (PPI) network was constructed and Molecular Complex Detection (MCODE) combined with cytoHubba plugins were used to identify key immune-related genes that may play an important role in the occurrence and progression of AMI. Results The CIBERSORT results suggested that there was a decrease in the infiltration of CD8 + T cells, gamma delta (γδ) T cells, and resting mast cells, along with an increase in the infiltration of neutrophils and M0 macrophages in AMI patients. Then, two modules (midnightblue and lightyellow) that were significantly correlated with AMI were identified, and the salmon module was found to be significantly associated with memory B cells. Gene enrichment analysis indicated that the 1,171 genes included in the salmon module are mainly involved in immune-related biological processes. MCODE analysis was used to identify four different MCODE complexes in the salmon module, while four hub genes (EEF1B2, RAC2, SPI1, and ITGAM) were found to be significantly correlated with AMI. The correlation analysis between the key genes and infiltrating immune cells showed that SPI1 and ITGAM were positively associated with neutrophils and M0 macrophages, while they were negatively associated with CD8 + T cells, γδ T cells, regulatory T cells (Tregs), and resting mast cells. The RT-qPCR validation results found that the expression of the ITGAM and SPI1 genes were significantly elevated in the AMI samples compared with the samples from healthy individuals, and the ROC curve analysis showed that ITGAM and SPI1 had a high diagnostic efficiency for the recognition of AMI. Conclusions Immune cell infiltration plays a crucial role in the occurrence and development of AMI. ITGAM and SPI1 are key immune-related genes that are potential novel targets for the prevention and treatment of AMI.

Novel diagnostic biomarkers related to necroptosis and immune infiltration landscape in acute myocardial infarction

“…We also detected that the immune cells were dramatically different in their levels in AMI vs . normal groups, conforming to prior results ( Ong et al, 2018 ; Wu et al, 2022 ; Xie et al, 2021 ; Zheng et al, 2022 ). Numerous diverse factors are involved in the inflammatory response in AMI, including neutrophils, monocytes, macrophages, B lymphocytes and CD8+ T cells ( Ong et al, 2018 ).…”

m6A regulator-mediated RNA methylation modification patterns are involved in the regulation of the immune microenvironment in ischaemic cardiomyopathy

“…Yang et al have suggested that monocyte infiltration increased and CD8 + T-cell infiltration decreased in patients with CAD 5 . Moreover, in a recently published study, we found that the infiltration of M0 macrophages and neutrophils increased, whereas the infiltration of CD8 + T cells, gamma delta (γδ) T cells, and resting mast cells decreased in patients with acute myocardial infarction (AMI) 6 . These results suggest that the immune mechanism plays a key role in atherosclerosis and related cardiovascular diseases.…”

Aging-related genes related to the prognosis and the immune microenvironment of acute myeloid leukemia

“…Worse, although most patients improve with treatment, a significant number of patients eventually relapse, which contributes to the low survival rate of AML. In recent years, there has been a lot of interest in predictive models for predicting the prognosis of AML (15,16). While the role of ARGs in AML and their potential mechanisms are still not elucidated, this study constructed and validated ARG risk models and predicted their potential impact on the prognosis of AML patients.…”