Integrated Gene Expression Profiling Analysis Reveals Potential Molecular Mechanisms and Candidate Biomarkers for Early Risk Stratification and Prediction of STEMI and Post-STEMI Heart Failure Patients.

Jing Xu,Yuejin Yang

doi:10.3389/fcvm.2021.736497

Abstract

Objective: To explore the molecular mechanism and search for the candidate differentially expressed genes (DEGs) with the predictive and prognostic potentiality that is detectable in the whole blood of patients with ST-segment elevation (STEMI) and those with post-STEMI HF.Methods: In this study, we downloaded GSE60993, GSE61144, GSE66360, and GSE59867 datasets from the NCBI-GEO database. DEGs of the datasets were investigated using R. Gene ontology (GO) and pathway enrichment were performed via ClueGO, CluePedia, and DAVID database. A protein interaction network was constructed via STRING. Enriched hub genes were analyzed by Cytoscape software. The least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and receiver operating characteristics analyses were performed to build machine learning models for predicting STEMI. Hub genes for further validated in patients with post-STEMI HF from GSE59867.Results: We identified 90 upregulated DEGs and nine downregulated DEGs convergence in the three datasets (|log2FC| ≥ 0.8 and adjusted p < 0.05). They were mainly enriched in GO terms relating to cytokine secretion, pattern recognition receptors signaling pathway, and immune cells activation. A cluster of eight genes including ITGAM, CLEC4D, SLC2A3, BST1, MCEMP1, PLAUR, GPR97, and MMP25 was found to be significant. A machine learning model built by SLC2A3, CLEC4D, GPR97, PLAUR, and BST1 exerted great value for STEMI prediction. Besides, ITGAM and BST1 might be candidate prognostic DEGs for post-STEMI HF.Conclusions: We reanalyzed the integrated transcriptomic signature of patients with STEMI showing predictive potentiality and revealed new insights and specific prospective DEGs for STEMI risk stratification and HF development.

Highlights

Acute myocardial infarction (AMI) is a consequence of rupture or erosion of a vulnerable, lipid-laden, chronic atherosclerotic coronary plaque, resulting in acute interruption of myocardial blood flow and ischemic myocardial necrosis, which remains a common cardiac emergency incidence with substantial morbidity and mortality worldwide [1, 2]
Microarray data profiles of GSE60993, GSE61144, GSE66360, and GSE59867 were downloaded from the Gene Expression Omnibus database, an international public repository from the National Center for Biotechnology Information (NCBI) that provides free access to full sets of genome data submitted by the research community [19]
Gene ontology analysis of the common differentially expressed genes (DEGs) was conducted via the DAVID database, as well as ClueGO and CluePedia tool kits in Cytoscape

Summary

Introduction

Acute myocardial infarction (AMI) is a consequence of rupture or erosion of a vulnerable, lipid-laden, chronic atherosclerotic coronary plaque, resulting in acute interruption of myocardial blood flow and ischemic myocardial necrosis, which remains a common cardiac emergency incidence with substantial morbidity and mortality worldwide [1, 2]. It has been recognized for decades that most atherosclerotic lesions underlying AMI are only partial luminal narrowing prior to acute plaque rupture and not obstructing the coronary blood flow [6,7,8]. Acute myocardial infarction has traditionally been classified on the basis of the presence or absence of ST-segment elevation (STEMI or non-STEMI) on the ECG. STEMI is a major killer in both elderly and non-elderly (age < 65 years) patients [9], but survivors of acute STEMI are prone to develop progressive ventricular remodeling and dysfunction that leads to heart failure (HF) [10,11,12]

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Conclusion