scholarly journals Network-Based Coexpression Analysis Identifies Functional and Prognostic Long Noncoding RNAs in Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jianguo Li ◽  
Jin Zhou ◽  
Shuangshuang Kai ◽  
Can Wang ◽  
Daijun Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Primary Liver Cancer ◽  
Noncoding Rnas ◽  
Enrichment Analysis ◽  
Long Noncoding Rnas ◽  
Coexpression Network ◽  
Functional Modules ◽  
Biological Processes ◽  
Coexpression Network Analysis

Hepatocellular carcinoma (HCC) is a primary liver cancer associated with a growing incidence and extremely high mortality. However, the pathogenic mechanism is still not fully understood. In the present study, we identified 1,631 upregulated and 1,515 downregulated genes and found that cell cycle and metabolism-related pathways or biological processes highly dysregulated in HCC. To assess the biological importance of these DEGs, we carried out weighted gene coexpression network analysis (WGCNA) to identify the functional modules potentially involved in HCC pathogenesis or progression. The five modules were detected with Dynamic Tree Cut algorithm, and GO enrichment analysis revealed that these modules exhibited different biological processes or signaling pathways, such as metabolism-related pathways, cell proliferation-related pathways, and molecules in tumor microenvironment. Moreover, we also observed two immune cells, namely, cytotoxic cells and macrophage enriched in modules grey and brown, respectively, while T helper cell-2 (Th2) was enriched in module turquoise. Among the WGCNA network, four hub long noncoding RNAs (lncRNAs) were identified to be associated with HCC prognostic outcomes, suggesting that coexpression network analysis could uncover lncRNAs with functional importance, which may be associated with prognostic outcomes of HCC patients. In summary, this study demonstrated that network-based analysis could identify some functional modules and some hub-lncRNAs, which may be critical for HCC pathogenesis or progression.

Microarray Profiling and Coexpression Network Analysis of Long Noncoding RNAs in Adipose Tissue of Obesity‐T2DM Mouse

Obesity ◽  
2019 ◽  
Vol 27 (10) ◽  
pp. 1644-1651 ◽  
Author(s):  
Zhenyu Yao ◽  
Chang Liu ◽  
Xiangfang Yu ◽  
Jun Meng ◽  
Bin Teng ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Network Analysis ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Coexpression Network ◽  
Microarray Profiling ◽  
Coexpression Network Analysis

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.

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 Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Lang Wang ◽  
Jun Hu ◽  
Jiali Zhou ◽  
Fan Guo ◽  
Tan Yao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Signaling Pathway ◽  
Noncoding Rnas ◽  
Mapk Signaling ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Signaling System ◽  
Pathway Network ◽  
Artery Disease ◽  
Coexpression Network Analysis

Background. Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD. Materials and Methods. A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates. Results. A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI. Conclusion. FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis.

scholarly journals Potential targets and molecular mechanism of miR-331-3p in hepatocellular carcinoma identified by weighted gene coexpression network analysis

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Qingjia Chi ◽  
Xinge Geng ◽  
Kang Xu ◽  
Chunli Wang ◽  
Han Zhao
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Tumor Tissues ◽  
Coexpression Network Analysis

Abstract Hepatocellular carcinoma (HCC) is one of the most common malignant tumor. miR-331-3p has been reported relevant to the progression of HCC, but the molecular mechanism of its regulation is still unclear. In the study, we comprehensively studied the role of miR-331-3p in HCC through weighted gene coexpression network analysis (WGCNA) based on The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Oncomine. WGCNA was applied to build gene co-expression networks to examine the correlation between gene sets and clinical characteristics, and to identify potential biomarkers. Five hundred one target genes of miR-331-3p were obtained by overlapping differentially expressed genes (DEGs) from the TCGA database and target genes predicted by miRWalk. The critical turquoise module and its eight key genes were screened by WGCNA. Enrichment analysis was implemented based on the genes in the turquoise module. Moreover, 48 genes with a high degree of connectivity were obtained by protein–protein interaction (PPI) analysis of the genes in the turquoise module. From overlapping genes analyzed by WGCNA and PPI, two hub genes were obtained, namely coatomer protein complex subunit zeta 1 (COPZ1) and elongation factor Tu GTP binding domain containing 2 (EFTUD2). In addition, the expression of both hub genes was also significantly higher in tumor tissues compared with normal tissues, as confirmed by analysis based on TCGA and Oncomine. Both hub genes were correlated with poor prognosis based on TCGA data. Receiver operating characteristic (ROC) curve validated that both hub genes exhibited excellent diagnostic efficiency for normal and tumor tissues.

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