scholarly journals Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis

2020 ◽  
Vol 26 ◽  
Author(s):  
Zongjin Li ◽  
Jacqueline Chyr ◽  
Zeyu Jia ◽  
Lina Wang ◽  
Xi Hu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

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 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.

scholarly journals Integrated Transcriptomic Analysis Reveals the Molecular Mechanism of Meningiomas by Weighted Gene Coexpression Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Biao Yang ◽  
Shuxun Wei ◽  
Yan-Bin Ma ◽  
Sheng-Hua Chu
Keyword(s):  
Network Analysis ◽  
Biological Significance ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Primary Intracranial Tumor ◽  
Coexpression Network Analysis ◽  
Potential Biomarkers

Meningiomas are the most common primary intracranial tumor in adults. However, to date, systemic coexpression analyses for meningiomas fail to explain its pathogenesis. The aim of the present study was to construct coexpression modules and identify potential biomarkers associated with meningioma progression. Weighted gene coexpression network analysis (WGCNA) was performed based on GSE43290, and module preservation was tested by GSE74385. Functional annotations were performed to analyze biological significance. Hub genes were selected for efficacy evaluations and correlation analyses using two independent cohorts. A total of 14 coexpression modules were identified, and module lightcyan was significantly associated with WHO grades. Functional enrichment analyses of module lightcyan were associated with tumor pathogenesis. The top 10 hub genes were extracted. Ten biomarkers, particularly AHCYL2, FGL2, and KCNMA1, were significantly related to grades and prognosis of meningioma. These findings not only construct coexpression modules leading to the better understanding of its pathogenesis but also provide potential biomarkers that represent specific on tumor grades and identify recurrence, predicting prognosis and progression of meningiomas.

scholarly journals Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Tingting Zhang ◽  
Nanyang Liu ◽  
Wei Wei ◽  
Zhen Zhang ◽  
Hao Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Network Analysis ◽  
Roc Curve ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis ◽  
Six Genes

Background. Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. Methods. In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣ log 2 FoldChange ∣ > 0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. Results. In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD ( r = 0.64 , P = 3 e − 20 ), and it was visualized by establishing a protein–protein interaction network. Moreover, this module was particularly enriched in “pathways of neurodegeneration—multiple diseases,” “Alzheimer disease,” “oxidative phosphorylation,” and “proteasome.” Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve ( AUC = 0.940 ). Conclusions. Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

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

2020 ◽  
Author(s):  
Baiyang Yu ◽  
Jianbin Liu ◽  
Di Wu ◽  
Ying Liu ◽  
Weijian Cen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Drought Resistance ◽  
Differentially Expressed ◽  
Chromosome 1 ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Go Terms ◽  
Coexpression Network Analysis

Abstract Background: Drought stress is an adverse factor with deleterious effects on several aspects of rice growth. However, the mechanism underlying drought resistance in rice remains unclear. To understand the molecular mechanism of the drought response in rice, drought-sensitive CSSL (Chromosome Single-substitution Segment Line) PY6 was used to map QTLs of sensitive phenotypes and to reveal the impact of the QTLs on transcriptional profiling.Results: The QTL dss-1 was mapped onto the short arm of chromosome 1 of rice. According to transcriptomic analysis, the identified differentially expressed genes (DEGs) exhibited a downregulated pattern and were mainly enriched in photosynthesis-related GO terms, indicating that photosynthesis was greatly inhibited under drought. Further, according to weighted gene coexpression network analysis (WGCNA), specific gene modules (designating a group of genes with a similar expression pattern) were strongly correlated with H2O2 (4 modules) and MDA (3 modules), respectively. Likewise, GO analysis revealed that the photosynthesis-related GO terms were consistently overrepresented in H2O2-correlated modules. Functional annotation of the differentially expressed hub genes (DEHGs) in the H2O2 and MDA-correlated modules revealed cross-talk between abiotic and biotic stress responses for these genes, which were annotated as encoding WRKYs and PR family proteins, were notably differentially expressed between PY6 and PR403.Conclusions: We speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage. Our results shed light on and provide deep insight into the drought resistance mechanism in rice.

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