Background:
Acute myocardial infarction (AMI) is a fatal disease that causes high morbidity and mortality. It has been reported that AMI is associated with immune cell infiltration. Now, we aimed to identify the potential diagnostic biomarkers of AMI and uncover the immune cell infiltration profile of AMI.
Methods:
From the Gene Expression Omnibus (GEO) data set, three data sets (GSE48060, GSE60993, and GSE66360) were downloaded. Differentially expressed genes (DEGs) from AMI and healthy control samples were screened. Furthermore, DEGs were performed gene ontology (GO) functional and kyoto encyclopedia of genes and genome (KEGG) pathway analyses. The Gene set enrichment analysis (GSEA) was used to analyze GO terms and KEGG pathways. Utilizing the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction (PPI) network was constructed, and the hub genes were identified. Then, the receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic value of hub genes. And, the diagnostic value of hub genes was further validated in an independent data set GSE61144. Finally, CIBERSORT was used to represent the compositional patterns of the 22 types of immune cell fractions in AMI.
Results:
A total of 71 DEGs were identified. These DEGs were mainly enriched in immune response and immune-related pathways. Toll-like receptor 2 (TLR2), interleukin-1B (IL1B), leukocyte immunoglobulin-like receptor subfamily B2 (LILRB2), Fc fragment of IgE receptor Ig (FCER1G), formyl peptide receptor 1 (FPR1), and matrix metalloproteinase 9 (MMP9) were identified as diagnostic markers with the value of < 0.05. Also, the immune cell infiltration analysis indicated that TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 were correlated with neutrophils, monocytes, resting natural killer (NK) cells, gamma delta T cells, and CD4 memory resting T cells. The fractions of monocytes and neutrophils were significantly higher in AMI tissues than in control tissues.
Conclusion:
TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 are involved in the process of AMI, which can be used as molecular biomarkers for the screening and diagnosis of AMI. In addition, the immune system plays a vital role in the occurrence and progression of AMI.
Citing Articles
Identification and validation of key genes associated with cell senescence in acute myocardial infarction.
Zhao W, Zhu G, Chu T, Wu L, Li H, Zhen Q
Front Cardiovasc Med. 2025; 12:1499157.
PMID: 40046960
PMC: 11880263.
DOI: 10.3389/fcvm.2025.1499157.
Integration of Single Cell and Bulk RNA-Sequencing Reveals Key Genes and Immune Cell Infiltration to Construct a Predictive Model and Identify Drug Targets in Endometriosis.
Zhang H, Fang Y, Luo D, Li Y
J Inflamm Res. 2025; 18:2783-2804.
PMID: 40026309
PMC: 11871914.
DOI: 10.2147/JIR.S497643.
Single-cell and bulk RNA sequencing-based screening and identification of extracellular trap network-related genes in neutrophils in acute myocardial infarction.
Li W, Yang J
Medicine (Baltimore). 2025; 103(47):e40590.
PMID: 39809140
PMC: 11596368.
DOI: 10.1097/MD.0000000000040590.
Integrating machine learning and single-cell transcriptomic analysis to identify potential biomarkers and analyze immune features of ischemic stroke.
Zhao Y, Ma X, Meng X, Li H, Tang Q
Sci Rep. 2024; 14(1):26069.
PMID: 39478056
PMC: 11525974.
DOI: 10.1038/s41598-024-77495-3.
FASLG as a Key Member of Necroptosis Participats in Acute Myocardial Infarction by Regulating Immune Infiltration.
Jia H, An F, Zhang Y, Yan M, Zhou Y, Bian H
Cardiol Res. 2024; 15(4):262-274.
PMID: 39205966
PMC: 11349138.
DOI: 10.14740/cr1652.
Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study.
Yang X, Huang Y, Tang D, Yue L
PLoS One. 2024; 19(7):e0305532.
PMID: 39024234
PMC: 11257238.
DOI: 10.1371/journal.pone.0305532.
Unraveling shared molecular signatures and potential therapeutic targets linking psoriasis and acute myocardial infarction.
Yang Z, Li J, Song H, Mei Z, Zhang S, Wu H
Sci Rep. 2024; 14(1):16471.
PMID: 39014096
PMC: 11252138.
DOI: 10.1038/s41598-024-67350-w.
Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation.
Zhu Y, Chen Y, Zu Y
J Transl Med. 2024; 22(1):612.
PMID: 38956669
PMC: 11221097.
DOI: 10.1186/s12967-024-05415-0.
Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.
Jin N, Rong J, Chen X, Huang L, Ma H
BMC Cardiovasc Disord. 2024; 24(1):272.
PMID: 38783198
PMC: 11118734.
DOI: 10.1186/s12872-024-03907-x.
Comprehensive bioinformatics analytics and validation reveal SLC31A1 as an emerging diagnostic biomarker for acute myocardial infarction.
Zhou S, Wang L, Huang X, Wang T, Tang Y, Liu Y
Aging (Albany NY). 2024; 16(9):8361-8377.
PMID: 38713173
PMC: 11132003.
DOI: 10.18632/aging.205199.
Identification and immunoinfiltration analysis of key genes in ulcerative colitis using WGCNA.
Ni S, Liu Y, Zhong J, Shen Y
PeerJ. 2024; 12:e16921.
PMID: 38426148
PMC: 10903335.
DOI: 10.7717/peerj.16921.
Novel diagnostic biomarkers related to necroptosis and immune infiltration landscape in acute myocardial infarction.
Wu W, Fan H, Cen J, Huang P, Li G, Tan Y
PeerJ. 2024; 12:e17044.
PMID: 38426147
PMC: 10903340.
DOI: 10.7717/peerj.17044.
Identification of the feature genes involved in cytokine release syndrome in COVID-19.
Yang B, Pan M, Feng K, Wu X, Yang F, Yang P
PLoS One. 2024; 19(1):e0296030.
PMID: 38165854
PMC: 10760774.
DOI: 10.1371/journal.pone.0296030.
M6A regulator methylation patterns and characteristics of immunity in acute ST-segment elevation myocardial infarction.
Yang J, Shangguan Q, Xie G, Yang M, Sheng G
Sci Rep. 2023; 13(1):15688.
PMID: 37735234
PMC: 10514189.
DOI: 10.1038/s41598-023-42959-5.
Neutrophil extracellular trap is an important connection between hemodialysis and acute myocardial infarction.
Yang Y, Jiao Y, Zhang Z, Zhuo L, Li W
Ren Fail. 2023; 45(1):2216307.
PMID: 37246754
PMC: 10228304.
DOI: 10.1080/0886022X.2023.2216307.
Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized.
Pan H, Yuan N, He L, Sheng J, Hu H, Zhai C
Funct Integr Genomics. 2023; 23(2):160.
PMID: 37178159
DOI: 10.1007/s10142-023-01081-5.
Construction of the ceRNA network in the progression of acute myocardial infarction.
Liu H, Qin S, Zhao Y, Gao L, Zhang C
Am J Cardiovasc Dis. 2023; 12(6):283-297.
PMID: 36743510
PMC: 9890199.
Identification and validation of senescence-related genes in circulating endothelial cells of patients with acute myocardial infarction.
Xiang J, Shen J, Zhang L, Tang B
Front Cardiovasc Med. 2022; 9:1057985.
PMID: 36582740
PMC: 9792765.
DOI: 10.3389/fcvm.2022.1057985.