ArticleOxidative medicine and cellular longevity2021
Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis.
Article in Oxidative medicine and cellular longevity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Bioinformatics and Experimental Validation of Diagnostic Marker Genes for Myocardial Infarction and Analysis of Their Immune Cell Infiltration.Biochemical genetics · 2026Article
- Machine learning-derived identification of an obesity and lipid metabolism-related genes signature for the diagnosis and molecular typing of acute myocardial infarction.Frontiers in cardiovascular medicine · 2026Article
- An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods.Scientific reports · 2025Article
- Ecdysterone Alleviates Atherosclerosis by Inhibiting NCF2 and Inhibiting Ferroptosis Mediated by the PI3K/Akt/Nrf2 Pathway.Journal of cellular and molecular medicine · 2025Article
- FASLG as a Key Member of Necroptosis Participats in Acute Myocardial Infarction by Regulating Immune Infiltration.Cardiology research · 2024Article
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- Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication.Journal of inflammation research · 2024Article
- Neutrophil extracellular trap is an important connection between hemodialysis and acute myocardial infarction.Renal failure · 2023Article
- Network Pharmacology Combined with Machine Learning to Reveal the Action Mechanism of Licochalcone Intervention in Liver Cancer.International journal of molecular sciences · 2023Article
- Expression Profiles of Long Non-Coding RNAs in the Articular Cartilage of Rats Exposed to T-2 Toxin.International journal of molecular sciences · 2023Article
- Cuproptosis-related genes prediction feature and immune microenvironment in major depressive disorder.Heliyon · 2023Article
- Computer-Aided Screening and Revealing Action Mechanism of Green Tea Polyphenols Intervention in Alzheimer's Disease.Foods (Basel, Switzerland) · 2023Article
- High expression of PI4K2A predicted poor prognosis of colon adenocarcinoma (COAD) and correlated with immunity.Cancer medicine · 2023Article
- A bioinformatics approach to identifying the biomarkers and pathogenesis of major depressive disorder combined with acute myocardial infarction.American journal of translational research · 2023Article
- Co-expression network analysis of frontal cortex during the progression of Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis.Journal of cardiovascular development and disease · 2022Article
- Shuangxinfang Prevents S100A9-Induced Macrophage/Microglial Inflammation to Improve Cardiac Function and Depression-Like Behavior in Rats After Acute Myocardial Infarction.Frontiers in pharmacology · 2022Article
- Identification of Potential Molecular Mechanism Related to Infertile Endometriosis.Frontiers in veterinary science · 2022Article
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9 authors.
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Abstract
backgroundIn the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI.
methodsThe Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset.
resultsA WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset.
conclusionsThe results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.
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