Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

Abstract Background: Acute myocardial infarction (AMI) is a common cause of death in many countries. Analyzing the potential biomarkers of AMI is crucial to understanding the molecular mechanism of disease. However, specific diagnostic biomarkers have not been fully elucidated, and candidate regulatory targets for AMI have not been determined.Methods: In this study, AMI gene chip data GSE48060, blood samples from normal cardiac function controls (n = 21) and AMI patients (n = 26) were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) of AMI and control group were identified with Online tool GEO2R. the genes co-expressed with were found. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, MR screening and cutoff threshold screening. Then, GO database was used to analyze the function and pathway enrichment of genes in the most important modules. KEGG DISEASE and BioCyc were used to analyze the hub gene in the module to determine important sub-pathways. In addition, the expression of hub genes were certified by RT-qPCR in AMI and control specimens.Results: This study identified 52 DEGs, including 26 up-regulated genes and 26 down-regulated genes. Co-expression network analysis of 52 DEGs revealed that there are mainly three up-regulated genes (AKR1C3, RPS24 and P2RY12) and three down-regulated genes (ACSL1, B3GNT5 and MGAM) as key hub genes in the co-expression network. Furthermore, GO enrichment analysis was performed on all AMI co-expression network genes and found to be functionally enriched mainly in RAGE receptor binding and negative regulation of T cell cytokine production. In addition, through KEGG DISEASE and BioCyc analysis, the functions of genes RPS24 and P2RY12 were enriched in cardiovascular diseases, AKR1C3 was enriched in cardenolide biosynthesis, MGAM was enriched in glycogenolysis, B3GNT5 was enriched in glycosphingolipids biosynthesis, and ACSL1 enriched in icosapentaenoate biosynthesis II. Conclusion: The hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential targets of AMI and have potential application value in the diagnosis of AMI.

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Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death

Journal of Proteome Research ◽

10.1021/acs.jproteome.9b00779 ◽

2020 ◽

Vol 19 (2) ◽

pp. 949-961 ◽

Cited By ~ 7

Author(s):

Alessia Vignoli ◽

Leonardo Tenori ◽

Betti Giusti ◽

Serafina Valente ◽

Nazario Carrabba ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Network Analysis ◽

Differential Network Analysis ◽

Clinical Scores ◽

Differential Network

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Identification of Candidate Genes and MicroRNAs for Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2019/5742608 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Yan Li ◽

Xiao_nan He ◽

Chao Li ◽

Ling Gong ◽

Min Liu

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Network Analysis ◽

Differentially Expressed Genes ◽

Microarray Data ◽

Differentially Expressed ◽

Coexpression Network ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis

Background. Identification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted. Methods. Microarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module. Results. A total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules. Conclusions. FCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.

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Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis

Oxidative Medicine and Cellular Longevity ◽

10.1155/2021/5553811 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yan Wang ◽

Xiangyang Zhang ◽

Min Duan ◽

Chenguang Zhang ◽

Ke Wang ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Network Analysis ◽

Interaction Network ◽

Coexpression Network ◽

Pathway Enrichment Analysis ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Key Genes ◽

Coexpression Network Analysis

Background. In 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. Methods. The 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. Results. A 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. Conclusions. The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

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