Weighted Gene Correlation Network Analysis Identifies Specific Functional Modules and Genes in Esophageal Cancer

Objective. Esophageal cancer (ESCA) is one of the most aggressive malignancies globally with an undesirable five-year survival rate. Here, this study was conducted for determining specific functional genes linked with ESCA initiation and progression. Methods. Gene expression profiling of ESCA was curated from TCGA (containing 160 ESCA and 11 nontumor specimens) and GSE38129 (30 paired ESCA and nontumor tissues) datasets. Differential expression analysis was conducted between ESCA and nontumor tissues with adjusted p value <0.05 and |log2fold-change|>1. Weighted gene coexpression network analysis (WGCNA) was conducted for determining the ESCA-specific coexpression modules and genes. Thereafter, ESCA-specific differentially expressed genes (DEGs) were intersected. Functional enrichment analysis was then presented with clusterProfiler package. Protein-protein interaction was conducted, and hub genes were determined. Association of hub genes with pathological staging was evaluated, and survival analysis was presented among ESCA patients. Results. This study determined 91 ESCA-specific DEGs following intersection of DEGs and ESCA-specific genes in TCGA and GSE38129 datasets. They were remarkably linked to cell cycle progression and carcinogenic pathways like the p53 signaling pathway, cellular senescence, and apoptosis. Ten ESCA-specific hub genes were determined, containing ASPM, BUB1B, CCNA2, CDC20, CDK1, DLGAP5, KIF11, KIF20 A, TOP2A, and TPX2. They were prominently associated with pathological staging. Among them, KIF11 upregulation was in relation to undesirable prognosis of ESCA patients. Conclusion. Collectively, we determined ESCA-specific coexpression modules and hub genes, which offered the foundation for future research concerning the mechanistic basis of ESCA.

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Detecting Hub Genes Associated With Lauren Subtype of Gastric Cancer by Weighted Gene Co-Expression Network Analysis

10.21203/rs.3.rs-61626/v1 ◽

2020 ◽

Author(s):

XU LIU ◽

Li Yao ◽

Jingkun Qu ◽

Lin Liu ◽

XU LIU ◽

...

Keyword(s):

Gastric Cancer ◽

Network Analysis ◽

Cancer Survival ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Diffuse Gastric Cancer ◽

Hub Genes ◽

Gene Modules

Abstract Background Gastric cancer is a rather heterogeneous type of malignant tumor. Among the several classification system, Lauren classification can reflect biological and pathological differences of different gastric cancer.Method to provide systematic biological perspectives, we employ weighted gene co-expression network analysis to reveal transcriptomic characteristics of gastric cancer. GSE15459 and TCGA STAD dataset were downloaded. Co-expressional network was constructed and gene modules were identified. Result Two key modules blue and red were suggested to be associated with diffuse gastric cancer. Functional enrichment analysis of genes from the two modules was performed. Validating in TCGA STAD dataset, we propose 10 genes TNS1, PGM5, CPXM2, LIMS2, AOC3, CRYAB, ANGPTL1, BOC and TOP2A to be hub-genes for diffuse gastric cancer. Finally these ten genes were associated with gastric cancer survival. Conclusion More attention need to be paid and further experimental study is required to elucidate the role of these genes.

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Hub genes of stroke identified by weighted gene co-expression network analysis

10.21203/rs.2.14681/v1 ◽

2019 ◽

Author(s):

Junhong Li ◽

Yang Zhai ◽

Peng Wu ◽

Yueqiang Hu ◽

Wei Chen ◽

...

Keyword(s):

Network Analysis ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Neuron Death ◽

Roc Curve Analysis ◽

Precise Diagnosis

Abstract Background Microarray-based gene expression profiling has been widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) can link microarray data directly to clinical traits and to identify rules for predicting pathological stage and prognosis of disease, it has been found useful in many biological processes. Stroke is one of the most common diseases worldwide, yet molecular mechanisms of its pathogenesis are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes associated with the pathogenesis of stroke.Results In this study, we screened out the differentially expressed genes from gene microarray expression profiles, then constructed the free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment and the receiver operating characteristic (ROC) curve analysis were performed. And the results show that a total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were found to be the most significantly related to stroke. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and the repair of DNA damage, while the yellow module was mainly enriched in ion transport system dysfunction which were correlated with neuron death. A total of 8 hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels (other datasets) and by existing literatures.Conclusions Eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of stroke in the future.

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Application of Weighted Gene Co-expression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

10.21203/rs.3.rs-254358/v1 ◽

2021 ◽

Author(s):

Mi Zhou ◽

Ruru Guo ◽

Yongfei Wang ◽

Wanling Yang ◽

Rongxiu Li ◽

...

Keyword(s):

Juvenile Idiopathic Arthritis ◽

Network Analysis ◽

Immune Responses ◽

Systemic Juvenile Idiopathic Arthritis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Data Sets ◽

Hub Genes ◽

Highly Correlated

Abstract Background: Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder whose molecular mechanism is still not clearly defined. To better understand the disease using scattered datasets from public domains, we performed a weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis.Methods: Two gene expression datasets, GSE7753 and GSE13501, were used to construct WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the entirety of genes and the hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the GWAS genes and used a consensus WGCNA analysis to prove that our conclusions are conservative and reproducible across multiple independent data sets. Results: A total of 5414 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=3e−29), while the green-yellow module was found to be closely related to the non-sJIA module (r =0.62, p=1e−14). Functional enrichment analysis demonstrated that the red module was largely enriched in activation of immune responses, infection, nucleosome and erythrocyte, 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, exemplified by genes such as KLRB1, KLRF1, CD160, KIRs etc.Conclusion: We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. The two modules may help understand the mechanisms of sJIA and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

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Identification of Neuropeptides as Potential Crosstalks Linking Down Syndrome and Periodontitis Revealed by Transcriptomic Analyses

Disease Markers ◽

10.1155/2021/7331821 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Yue Chen ◽

Xiaofei Yu ◽

Jia Kong

Keyword(s):

Down Syndrome ◽

Signaling Pathway ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Mapk Signaling ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Future Research ◽

Biological Processes

Background. This bioinformatics study was aimed to investigate the relationship between periodontitis (PD) and Down Syndrome (DS) regarding potential crosstalk genes, related neuropeptides, and biological processes. Methods. Data for PD (GSE23586, GSE10334 and GSE16134) and DS (GSE35665) were downloaded from NCBI Gene Expression Omnibus (GEO). Following normalization and merging of PD data, differential expression analysis was performed ( p value < 0.05 and ∣ log FC ∣ ≥ 0.5 ). The common deregulated genes between PD and DS were considered as crosstalk genes. The significantly differentially expressed genes were used to construct the coexpression network and to further identify coexpression gene modules. To acquire the significant modules, the significant expression level of genes in the module was used to analyze the enrichment of genes in each module. Neuropeptides were assessed from NeuroPedia database. Neuropeptide genes and crosstalk genes were merged and mapped into PPI network, and the correlation coefficient (Spearman) was determined for the crosstalk genes. Results. 138 crosstalk genes were predicted. According to the functional enrichment analysis, these genes significantly regulated different biological processes and pathways. In enrichment analysis, the significant module of DS was pink module, and turquoise module was significant in PD. Four common crosstalk genes were acquired, i.e., CD19, FCRL5, FCRLA, and HLA-DOB. In the complex network, INS and IGF2 interacted with CASP3 and TP53, which commonly regulated the MAPK signaling pathway. Moreover, the results showed that TP53 interacted with IGF2 and INS inducing the dysregulation of PI3K-Akt signaling pathway. UBL was positively correlated with crosstalk genes in both diseases. LEP was revealed to be both a neuropeptide and crosstalk gene and was positively correlated with other crosstalk genes. Conclusion. Different crosstalk genes, related neuropeptides, and biological pathways and processes were revealed between PD and DS, which can serve as a theoretical basis for future research.

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Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-915958/v1 ◽

2021 ◽

Author(s):

Jun Jiang ◽

Delong Chen ◽

Siyuan Xie ◽

Qichao Dong ◽

Yi Yu ◽

...

Keyword(s):

Network Analysis ◽

Hedgehog Signaling ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Hub Genes ◽

Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

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Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

Frontiers in Oncology ◽

10.3389/fonc.2021.542063 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiamei Liu ◽

Shengye Liu ◽

Xianghong Yang

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Network Analysis ◽

Cancer Progression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Gene Modules

BackgroundDespite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.MethodsGene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.ResultsUsing WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.ConclusionOur research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

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RBBP4 Enhances Platinum Chemo Resistance in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2021/6905985 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Nianwu Wang ◽

Wei Wang ◽

Wenli Mao ◽

Nazuke Kuerbantayi ◽

Nuan Jia ◽

...

Keyword(s):

Network Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Primary Treatment ◽

Functional Enrichment ◽

Hub Genes ◽

Hub Gene ◽

Prognostic Analysis ◽

Platinum Based Chemotherapy ◽

Gene Modules

Background. The majority of lung cancers are adenocarcinomas, with the proportion being 40%. The patients are mostly diagnosed in the middle and late stages with metastasis and easy recurrence, which poses great challenge to the treatment and prognosis. Platinum-based chemotherapy is a primary treatment for adenocarcinoma, which frequently causes drug resistance. As a result, it is important to uncover the mechanisms of the chemoresponse of adenocarcinoma to platinum-based chemotherapy. Methods. The genes from the dataset GSE7880 were gathered into gene modules with the assistance of weighted gene coexpression network analysis (WGCNA), the gene trait significance absolute value (|GS|), and gene module memberships (MM). The genes from hub gene modules were calculated with a protein-protein interaction (PPI) network analysis in order to obtain a screening map of hub genes. The hub genes with both a high |GS| and MM and a high degree were selected. Furthermore, genes in the hub gene modules also went through a Gene Ontology (GO) functional enrichment analysis. Results. 11 hub genes in four hub gene modules (LY86, ACTR2, CDK2, CKAP4, KPNB1, RBBP4, SMAD4, MYL6, RPS27, TSPAN2, and VAMP2) were chosen as the significant hub genes. Through the GO function enrichment analysis, it was indicated that four modules were abundant in immune system functions (floralwhite), amino acid biosynthetic process (lightpink4), cell chemotaxis (navajowhite2), and targeting protein (paleturquoise). Four hub genes with the highest |GS| were verified by prognostic analysis.

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Identification of Biomarkers and Pathogenesis in Severe Asthma by Co-expression Network Analysis

10.21203/rs.3.rs-123118/v1 ◽

2020 ◽

Author(s):

Zeyi Zhang ◽

Ou Chen ◽

Jingjing Wang

Keyword(s):

Network Analysis ◽

Severe Asthma ◽

Gene Networks ◽

Molecular Mechanisms ◽

Area Under The Curve ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Asthma Severity ◽

Functional Enrichment ◽

Hub Genes

Abstract BackgroundSevere asthma is a heterogeneous inflammatory disease. The rise of precise immunotherapy for severe asthmatics underlines more understanding of molecular mechanisms and biomarkers. In this study, we aim to identify underlying mechanisms and hub genes that define asthma severity.MethodsDifferentially expressed genes were screened out based on bronchial epithelial brushings from mild and severe asthmatics. Then, the weighted gene co-expression network analysis was adopted to identify gene networks and the most significant module associated with asthma severity. Meanwhile, hub genes screening and functional enrichment analysis was performed. Receiver operating characteristic was conducted to validate the hub genes.ResultsWeighted gene co-expression network analysis identified 6 modules associated with asthma severity. Three modules were positively correlated (P < 0.001) with asthma severity, containing genes upregulated in severe asthmatics. Functional enrichment analysis found genes in the highlighted module mainly enriched in neutrophil degranulation and activation, leukocyte migration and chemotaxis. Hub genes identified in the module were CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK. Combining these hub genes possessed a moderate ability for discriminating between severe asthmatics and mild-moderate asthmatics with an area under the curve of 0.75.ConclusionOur results identified biomarkers and potential pathogenesis of severe asthma, which provides sight into treatment targets and prognostic markers.

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Identification of biomarkers and pathogenesis in severe asthma by coexpression network analysis

BMC Medical Genomics ◽

10.1186/s12920-021-00892-4 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Zeyi Zhang ◽

Jingjing Wang ◽

Ou Chen

Keyword(s):

Network Analysis ◽

Severe Asthma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Asthma Severity ◽

Functional Enrichment ◽

Coexpression Network ◽

Validation Dataset ◽

Hub Genes ◽

Coexpression Network Analysis

Abstract Background Severe asthma is a heterogeneous inflammatory disease. The increase in precise immunotherapy for severe asthmatics requires a greater understanding of molecular mechanisms and biomarkers. In this study, we aimed to identify the underlying mechanisms and hub genes that determine asthma severity. Methods Differentially expressed genes (DEGs) were identified based on bronchial epithelial brushings from mild and severe asthmatics. Then, weighted gene coexpression network analysis (WGCNA) was used to identify gene networks and the module most significantly associated with asthma severity. Furthermore, hub gene screening and functional enrichment analysis were performed. Replication with another dataset was conducted to validate the hub genes. Results DEGs from 14 mild and 11 severe asthmatics were subjected to WGCNA. Six modules associated with asthma severity were identified. Three modules were positively correlated (P < 0.001) with asthma severity and contained genes that were upregulated in severe asthmatics. Functional enrichment analysis showed that genes in the most significant module were mainly enriched in neutrophil activation and degranulation, and cytokine receptor interaction. Hub genes included CXCR1, CXCR2, CCR1, CCR7, TLR2, FPR1, FCGR3B, FCGR2A, ITGAM, and PLEK; CXCR1, CXCR2, and TLR2 were significantly related to asthma severity in the validation dataset. The combination of ten hub genes exhibited a moderate ability to distinguish between severe and mild-moderate asthmatics. Conclusion Our results identified biomarkers and characterized potential pathogenesis of severe asthma, providing insight into treatment targets and prognostic markers.

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Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis

Bioscience Reports ◽

10.1042/bsr20202044 ◽

2020 ◽

Vol 40 (9) ◽

Author(s):

Peng Wang ◽

Huaixin Zheng ◽

Jiayu Zhang ◽

Yashu Wang ◽

Pingping Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Network Analysis ◽

Target Genes ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Gene Module ◽

Hub Genes ◽

Gene Modules ◽

Growth And Aging

Abstract Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method—Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.

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