Related Experiment Video
Updated: May 16, 2025
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
Published on: September 20, 2024
Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through
Huiying Li1,2, Qiwei Zhu1, Wei Wang3
1Department of Cardiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, 28 Fuxing Road, Haidian, Beijing, 100853, China.
Insights
Researchers identified four key genes (AKT3, GJC2, HMGCL, RBM17) in peripheral blood mononuclear cells that could improve diagnosis and treatment for ST-segment elevation myocardial infarction (STEMI) by reflecting immune cell infiltration.
Area of Science:
- Cardiology
- Immunology
- Bioinformatics
Background:
- ST-segment elevation myocardial infarction (STEMI) is a critical cardiac condition with a poor prognosis.
- Immune cell infiltration, particularly M1 macrophages, in the myocardium post-STEMI significantly impacts the cardiac immune microenvironment.
- Identifying reliable serum biomarkers for immune infiltration is crucial for effective STEMI diagnosis and treatment.
Purpose of the Study:
- To identify novel serum biomarkers associated with M1 macrophage infiltration and STEMI using integrated bioinformatics and machine learning.
- To stratify STEMI patients into subtypes based on molecular profiles.
- To validate the diagnostic and prognostic potential of identified biomarkers.
Main Methods:
- Utilized limma package, CIBERSORTx, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) networks on the GSE59867 dataset.
- Employed machine learning algorithms to identify key genes associated with STEMI subtypes and M1 macrophage infiltration.
- Validated gene expression and diagnostic value using GSE59867, GSE62646 datasets, and real-time PCR in STEMI patients.
Main Results:
- Identified AKT3, GJC2, HMGCL, and RBM17 as key genes linked to STEMI subtypes and M1 macrophage infiltration in the acute phase.
- These four genes, derived from peripheral blood mononuclear cells (PBMCs), showed significant diagnostic value.
- Expression profiles and diagnostic utility were confirmed across multiple datasets and patient cohorts.
Conclusions:
- AKT3, GJC2, HMGCL, and RBM17 are promising biomarkers for enhancing STEMI diagnosis accuracy.
- These biomarkers may offer potential therapeutic targets for STEMI patients.
- The study provides a comprehensive approach to biomarker discovery in STEMI through integrated bioinformatics and machine learning.
Abstract:
ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the increased number of circulating leukocytes including macrophages can lead to the accumulation of more cells in the myocardium, affecting the cardiac immune microenvironment. Identifying serum biomarkers associated with immune infiltration after STEMI is important for diagnosing and treating STEMI. In this work, we aimed to use integrated bioinformatics and machine learning methods to identify new biomarkers. First, candidate genes closely associated with M1 macrophage immune infiltration and STEMI were obtained using the limma package, the CIBERSORTx package, weighted gene coexpression network analysis (WGCNA), and protein‒protein interaction (PPI) networks from the GSE59867 dataset, which comprises peripheral blood mononuclear cell (PBMC) samples. The STEMI patients were subsequently stratified into subtypes using the ConsensusClusterPlus package. Furthermore, using machine learning methods, we identified AKT3, GJC2, HMGCL and RBM17 as the genes with the greatest potential to be associated with STEMI subtypes and with M1 macrophage infiltration during the acute phase of STEMI. Finally, the expression profile and diagnostic value of the four feature genes were validated in the GSE59867 and GSE62646 datasets and in 24 patients using real-time PCR. This study revealed logically and comprehensively that AKT3, GJC2, HMGCL and RBM17, which are derived from PBMCs, could enhance the accuracy of STEMI diagnosis and might provide effective treatment options for STEMI patients.
