Evidence map›Paper›PMID 38987688›Full record

ArticleBMC cardiovascular disorders2024

Identification of hub glycolysis-related genes in acute myocardial infarction and their correlation with immune infiltration using bioinformatics analysis.

Xiaoqing Zhang, Lina Zhang, Ya Gao, Zhangyu Liu, Kaizheng Gong

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Article in BMC cardiovascular disorders, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaoqing Zhang *Department of Cardiology, Affiliated Hospital of Yangzhou University, No. 368, Hanjiang Middle Road, Yangzhou 225000, Jiangsu, China.
Lina Zhang *Department of Cardiology, Affiliated Hospital of Nantong University, No.20, Xisi Road, Nantong 226001, Jiangsu, China.
Ya GaoDepartment of Cardiology, Affiliated Hospital of Yangzhou University, No. 368, Hanjiang Middle Road, Yangzhou 225000, Jiangsu, China.
Zhangyu LiuDepartment of Cardiology, Affiliated Hospital of Yangzhou University, No. 368, Hanjiang Middle Road, Yangzhou 225000, Jiangsu, China.
Kaizheng GongDepartment of Cardiology, Affiliated Hospital of Yangzhou University, No. 368, Hanjiang Middle Road, Yangzhou 225000, Jiangsu, China. yungkzh@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeGlycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration.

methodsDifferentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs.

resultsA total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting.

conclusionWe concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Indexed as

Computational BiologyGene Regulatory NetworksGlycolysisMyocardial InfarctionProtein Interaction MapsCase-Control StudiesDatabases, GeneticFemaleGene Expression ProfilingHexokinaseHumansLeukocytes, MononuclearMaleMiddle AgedNomogramsPredictive Value of TestsHexokinaseHK2 protein, humanAcute myocardial infarctionCIBERSORTGlycolysisImmune infiltrationNomogramROC curves

Identifiers

PMID38987688
PMCPMC11234719

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