Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

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A risk stratification model for lung cancer based on gene coexpression network

10.1101/179770 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hongyoon Choi ◽

Kwon Joong Na

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Risk Stratification ◽

Coexpression Network ◽

Gene Coexpression Network ◽

Training Set ◽

Gene Coexpression ◽

Risk Stratification Model ◽

Independent Test ◽

Network Modules

AbstractRisk stratification model for lung cancer with gene expression profile is of great interest. Instead of the previously reported models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray datasets obtained from lung adenocarcinoma, gene coexpression network analysis was performed to identify survival-related network modules. Representative genes of these network modules were selected and then, risk stratification model was constructed exploiting deep learning algorithm. The model was validated in two independent test cohorts. Survival analysis using univariate and multivariate Cox regression was performed using the output of the model to evaluate whether the model could predict patients’ overall survival independent of clinicopathological variables. Five network modules were significantly associated with patients’ survival. Considering prognostic significance and representativeness, genes of the two survival-related modules were selected for input data of the risk stratification model. The output of the model was significantly associated with patients’ overall survival in the two independent test sets as well as training set (p < 0.00001, p < 0.0001 and p = 0.02 for training set, test set 1 and 2, respectively). In multivariate analyses, the model was associated with patients’ prognosis independent of other clinical and pathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature, and the clinical application of deep learning in genomic data science for prognosis prediction.

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Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2021/4542995 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Aoran Yang ◽

Xinhuan Wang ◽

Yaofeng Hu ◽

Chao Shang ◽

Yang Hong

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Molecular Mechanisms ◽

The Cancer Genome Atlas ◽

Coexpression Network ◽

Normal Brain ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis ◽

Core Genes

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

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

PeerJ ◽

10.7717/peerj.8843 ◽

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.

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

BioMed Research International ◽

10.1155/2020/3876834 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8 ◽

Cited By ~ 1

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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Integrative analysis of transcriptome-wide association study and gene expression profiling identifies candidate genes associated with stroke

PeerJ ◽

10.7717/peerj.7435 ◽

2019 ◽

Vol 7 ◽

pp. e7435

Author(s):

Jian Yang ◽

Bin Yan ◽

Yajuan Fan ◽

Lihong Yang ◽

Binbin Zhao ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Candidate Genes ◽

Expression Profiling ◽

Enrichment Analysis ◽

Integrative Analysis ◽

Functional Gene ◽

Gene Sets ◽

Go Enrichment ◽

Go Enrichment Analysis

Background Stroke is a major public health burden worldwide. Although genetic variation is known to play a role in the pathogenesis of stroke, the specific pathogenic mechanisms are still unclear. Transcriptome-wide association studies (TWAS) is a powerful approach to prioritize candidate risk genes underlying complex traits. However, this approach has not been applied in stroke. Methods We conducted an integrative analysis of TWAS using data from the MEGASTROKE Consortium and gene expression profiling to identify candidate genes for the pathogenesis of stroke. Gene ontology (GO) enrichment analysis was also conducted to detect functional gene sets. Results The TWAS identified 515 transcriptome-wide significant tissue-specific genes, among which SLC25A44 (P = 5.46E−10) and LRCH1 (P = 1.54E−6) were significant by Bonferroni test for stroke. After validation with gene expression profiling, 19 unique genes were recognized. GO enrichment analysis identified eight significant GO functional gene sets, including regulation of cell shape (P = 0.0059), face morphogenesis (P = 0.0247), and positive regulation of ATPase activity (P = 0.0256). Conclusions Our study identified multiple stroke-associated genes and gene sets, and this analysis provided novel insights into the genetic mechanisms underlying stroke.

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Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Journal of Translational Medicine ◽

10.1186/s12967-021-03204-7 ◽

2022 ◽

Vol 20 (1) ◽

Author(s):

Zhenyuan Han ◽

Huiping Ren ◽

Jingjing Sun ◽

Lihui Jin ◽

Qin Wang ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Pleomorphic Adenoma ◽

Expression Profiles ◽

Functional Enrichment ◽

Coexpression Network ◽

Low Grade ◽

Gene Coexpression Network ◽

Gene Coexpression ◽

Coexpression Network Analysis

Abstract Background Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes. Methods RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log2 Fold change (FC)|> 1 and adjusted p value < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA. Results In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA. Conclusion FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

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Effects of Fibronectin 1 on Cell Proliferation, Senescence and Apoptosis of Human Glioma Cells Through the PI3K/AKT Signaling Pathway

Cellular Physiology and Biochemistry ◽

10.1159/000492096 ◽

2018 ◽

Vol 48 (3) ◽

pp. 1382-1396 ◽

Cited By ~ 39

Author(s):

Yu-Xiang Liao ◽

Zhi-Ping Zhang ◽

Jie Zhao ◽

Jing-Ping Liu

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Cell Proliferation ◽

Signaling Pathway ◽

Enrichment Analysis ◽

Akt Signaling ◽

Pathway Enrichment Analysis ◽

Akt Signaling Pathway ◽

Go Enrichment ◽

Go Enrichment Analysis

Background/Aims: The current study aimed to investigate the role by which fibronectin 1 (FN1) influences the cell cycle, senescence and apoptosis in human glioma cells through the PI3K/ AKT signaling pathway. Methods: Differentially expressed genes (DEGs) were identified based on gene expression data (GSE12657, GSE15824 and GSE45921 datasets) and probe annotation files from Gene Expression Omnibus. The DEGs were identified in connection with gene ontology (GO) enrichment analysis and with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The positive expression of the FN1 protein was detected by immunohistochemistry. The glioma cell lines U251 and T98G were selected and assigned into blank, negative control (NC) and siRNA-FN1 groups. A dual luciferase reporter gene assay was used to investigate the effects of FN1 on transcriptional activity through the PI3K/AKT signaling pathway. An MTT assay was applied for the detection of cell proliferation, while flow cytometry was employed for cell cycle stage and cellular apoptosis detection. β-galactosidase staining was utilized to detect cellular senescence, a scratch test was applied to evaluate cell migration, and a transwell assay was used to analyze cell invasion. Western blotting and qRT-PCR methods were used to detect the protein and mRNA expression levels, respectively, of the FN1 gene and the related genes in the PI3K/AKT pathway (PI3K, AKT and PTEN), the cell cycle (pRb, CDK4 and Cyclin D1) and cell senescence (p16 and p21) among the collected tissues and cells. Results: GSE12657 profiling revealed FN1 to be the most upregulated gene in glioma. Regarding the GSE12657 and GSE15824 datasets, FN1 gene expression was higher in glioma tissues than in normal tissues. GO enrichment analysis and KEGG pathway enrichment analysis indicated that FN1 is involved in the synthesis of extracellular matrix (ECM) components and the PI3K/AKT signaling pathway. Verification was provided, indicating the role played by the FN1 gene in the regulation of the PI3K/AKT signaling pathway, as silencing the FN1 gene was found to inhibit cell proliferation, promote cell apoptosis and senescence, and reduce migration and invasion through the down-regulation of FN1 gene expression and disruption of the PI3K-AKT signaling pathway. Conclusion: The findings of this study provide evidence highlighting the prominent role played by FN1 in stimulating glioma growth, invasion, and survival through the activation of the PI3K/AKT signaling pathway.

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Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis

BioMed Research International ◽

10.1155/2020/4169691 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Ming Yi ◽

Tianye Li ◽

Shuang Qin ◽

Shengnan Yu ◽

Qian Chu ◽

...

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Predictive Biomarkers ◽

Coexpression Network ◽

Mrna Levels ◽

Gene Coexpression Network ◽

Hub Genes ◽

Gene Coexpression ◽

Lung Adenocarcinomas ◽

Coexpression Network Analysis

Lung adenocarcinoma is the most frequently diagnosed subtype of nonsmall cell lung cancer. The molecular mechanisms of the initiation and progression of lung adenocarcinoma remain to be further determined. This study aimed to screen genes related to the progression of lung adenocarcinoma. By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients’ clinical traits, then further identify predictive biomarkers. GSE11969 was obtained from the Gene Expression Omnibus (GEO) database which contained the gene expression data of 90 lung adenocarcinoma patients. Data of the Cancer Genome Atlas (TCGA) were employed as the validation cohort. After the average linkage hierarchical clustering, a total of 9 modules were generated. In the clinical significant module (R = 0.44, P<0.0001), we identified 29 network hub genes. Subsequent verification in the TCGA database showed that 11 hub genes (ANLN, CDCA5, FLJ21924, LMNB1, MAD2L1, RACGAP1, RFC4, SNRPD1, TOP2A, TTK, and ZWINT) were significantly associated with poor survival data of lung adenocarcinomas. Besides, the results of receiver operating characteristic curves indicated that the mRNA levels of this group of genes exhibited high specificity and sensitivity to distinguish malignant lesions from nonmalignant tissues. Apart from mRNA levels, we found that the protein abundances of these 11 genes were remarkably upregulated in lung adenocarcinomas compared with normal tissues. In conclusion, by the WGCNA method, a panel of 11 genes were identified as predictive biomarkers for tumorigenesis and poor prognosis of lung adenocarcinomas.

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