scholarly journals Identification of Biomarkers Related to Immune Cell Infiltration with Gene Coexpression Network in Myocardial Infarction

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Lei Zhang ◽  
Qiqi Wang ◽  
Xudong Xie
Keyword(s):  
Myocardial Infarction ◽  
T Cell ◽  
Immune Cell ◽  
Nk Cell ◽  
Diagnostic Value ◽  
Coexpression Network ◽  
Immune Cell Infiltration ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression

Background. There is evidence that the immune system plays a key critical role in the pathogenesis of myocardial infarction (MI). However, the exact mechanisms associated with immunity have not been systematically uncovered. Methods. This study used the weighted gene coexpression network analysis (WGCNA) and the CIBERSORT algorithm to analyze the MI expression data from the Gene Expression Omnibus database and then identify the module associated with immune cell infiltration. In addition, we built the coexpression network and protein-protein interactions network analysis to identify the hub genes. Furthermore, the relationship between hub genes and NK cell resting was validated by using another dataset GSE123342. Finally, receiver operating characteristic (ROC) curve analyses were used to assess the diagnostic value of verified hub genes. Results. Monocytes and neutrophils were markedly increased, and T cell CD8, T cell CD4 naive, T cell CD4 memory resting, and NK cell resting were significantly decreased in MI groups compared with stable coronary artery disease (CAD) groups. The WGCNA results showed that the pink model had the highest correlation with the NK cell resting infiltration level. We identified 11 hub genes whose expression correlated to the NK cell resting infiltration level, among which, 7 hub genes (NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES) were successfully validated in GSE123342. And these 7 genes had diagnostic value to distinguish MI and stable CAD. Conclusions. NKG7, TBX21, PRF1, CD247, KLRD1, FASLG, and EOMES may be a diagnostic biomarker and therapeutic target associated with NK cell resting infiltration in MI.

scholarly journals Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guoju Fan ◽  
Zhihai Jin ◽  
Kaiqiang Wang ◽  
Huitang Yang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Venous Thromboembolism ◽  
Network Analysis ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Set ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Abstract Background The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT. Methods Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database. Results In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT. Conclusions These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

scholarly journals Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

2019 ◽  
Vol 49 (10) ◽  
pp. 1195-1206 ◽  
Author(s):  
Aiping Tian ◽  
Ke Pu ◽  
Boxuan Li ◽  
Min Li ◽  
Xiaoguang Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

scholarly journals Correction to: Weighted gene coexpression network analysis-based identification of key modules and hub genes associated with drought sensitivity in rice

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Drought Sensitivity ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

An amendment to this paper has been published and can be accessed via the original article.

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

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
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.

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

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.

scholarly journals Application of Weighted Gene Coexpression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mi Zhou ◽  
Ruru Guo ◽  
Yong-Fei Wang ◽  
Wanling Yang ◽  
Rongxiu Li ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Network Analysis ◽  
Immune Responses ◽  
Systemic Juvenile Idiopathic Arthritis ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder with a still not clearly defined molecular mechanism. To better understand the disease, we used scattered datasets from public domains and performed a weighted gene coexpression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis. Two gene expression datasets, GSE7753 and GSE13501, were used to construct the WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the genes and hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the genome-wide association study (GWAS) genes and used a consensus WGCNA to verify that our conclusions were conservative and reproducible across multiple independent datasets. A total of 5,414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module ( r = 0.8 , p = 3 e − 29 ), whereas the green-yellow module was found to be closely related to the non-sJIA module ( r = 0.62 , p = 1 e − 14 ). Functional enrichment analysis demonstrated that the red module was mostly enriched in the activation of immune responses, infection, nucleosomes, and erythrocytes, and the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58, and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, as exemplified by the genes KLRB1, KLRF1, CD160, and KIRs. We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. Particularly, the modules may help understand the mechanisms of sJIA, and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

scholarly journals Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8843
Author(s):  
Dongmei Guo ◽  
Hongchun Wang ◽  
Li Sun ◽  
Shuang Liu ◽  
Shujing Du ◽  
...  
Keyword(s):  
Network Analysis ◽  
Cell Lymphoma ◽  
Enrichment Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Mantle Cell ◽  
Coexpression Network Analysis ◽  
Blue Module

Purpose Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL. Methods The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein–protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan–Meier method and were compared using the log-rank test. Results Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05). Conclusions The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

Identification of potential proteases for abdominal aortic aneurysm by weighted gene coexpression network analysis

Genome ◽  
2020 ◽  
Vol 63 (11) ◽  
pp. 561-575
Author(s):  
Hui Zhang ◽  
Dan Yang ◽  
Siliang Chen ◽  
Fangda Li ◽  
Liqiang Cui ◽  
...  
Keyword(s):  
Abdominal Aortic Aneurysm ◽  
Network Analysis ◽  
Aortic Aneurysm ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Abdominal Aortic ◽  
T Cell Regulation ◽  
Coexpression Network Analysis

Proteases are involved in the degradation of the extracellular matrix (ECM), which contributes to the formation of abdominal aortic aneurysm (AAA). To identify new disease targets in addition to the results of previous microarray studies, we performed next-generation sequencing (NGS) of the whole transcriptome of Angiotensin II-treated ApoE−/− male mice (n = 4) and control mice (n = 4) to obtain differentially expressed genes (DEGs). Identified DEGs of proteases were analyzed using weighted gene coexpression network analysis (WGCNA). RT-qPCR was conducted to validate the differential expression of selected hub genes. We found that 43 DEGs were correlated with the expression of the protease profile, and most were clustered in immune response module. Among 26 hub genes, we found that Mmp16 and Mmp17 were significantly downregulated in AAA mice, while Ctsa, Ctsc, and Ctsw were upregulated. Our functional annotation analysis of genes coexpressed with the five hub genes indicated that Ctsw and Mmp17 were involved in T cell regulation and Cell adhesion molecule pathway, respectively, and that both were involved in general regulation of the cell cycle and gene expression. Overall, our data suggest that these ectopic genes are potentially crucial to AAA formation and may act as biomarkers for the diagnosis of AAA.

scholarly journals Identification of Genes Associated with the Metastasis of Pheochromocytoma/Paraganglioma Based on Weighted Gene Coexpression Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Qisheng Su ◽  
Qinpei Ding ◽  
Zunni Zhang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Neuroendocrine Neoplasm ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Pheochromocytoma/paraganglioma (PCPG) is a benign neuroendocrine neoplasm in most cases, but metastasis and other malignant behaviors can be observed in this tumor. The aim of this study was to identify genes associated with the metastasis of PCPG. Methods. The Cancer Genome Atlas (TCGA) expression profile data and clinical information were downloaded from the cbioportal, and the weighted gene coexpression network analysis (WGCNA) was conducted. The gene coexpression modules were extracted from the network through the WGCNA package of R software. We further extracted metastasis-related modules of PCPG. Enrichment analysis of Biological Process of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes was carried out for important modules, and survival analysis of hub genes in the modules was performed. Results. A total of 168 PCPG samples were included in this study. The weighted gene coexpression network was constructed with 5125 genes of the top 25% variance among the 20501 genes obtained from the database. We identified 11 coexpression modules, among which the salmon module was associated with the age of PCPG patients at diagnosis, metastasis, and malignancy of the tumors. Conclusion. WGCNA was performed to identify the gene coexpression modules and hub genes in the metastasis-related gene module of PCPG. The findings in this study provide a new clue for further study of the mechanisms underlying the PCPG metastasis.

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