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

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.

scholarly journals Weighted gene co-expression network analysis identifies potential candidate biomarkers for adenocarcinoma of the esophagogastric junction

2020 ◽  
Author(s):  
Zhiyong Lai ◽  
Wenhui Yang ◽  
Weibin Li ◽  
Tiantian Zhang ◽  
Kai Jia ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Network Analysis ◽  
Esophagogastric Junction ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Potential Candidate ◽  
Hub Genes ◽  
Analysis Strategy ◽  
Gene Modules

Abstract Background: Gastric cancer (GC) is the fifth most common kind of malignant tumor, and commonly leads to death. As a subtype of gastric cancer, adenocarcinoma of the esophagogastric junction (AEG), accounts for about 50% of all gastric cancer cases. So far, the systematic co-expression analysis of this tumor has not fully explained its pathogenesis. The purpose of this study was to construct RNAs co-expression networks to predict candidate hub genes associated with the tumorigenesis of AEG. Methods: The RNA-seq data of 22 AEG patients was processed with weighted gene co-expression network analysis strategy. Differentiate the modules with clinical tumor markers and preservation, and carry out gene ontology and pathway enrichment analysis. We identified the co-expression modules and used GO and KEGG terms to investigated the functional enrichment of co-expression genes, suggesting that blue and brown modules are related to the biological processes of tumorigenesis. Results: Twenty-five distinct co-expression gene modules were identified, and as the top hub genes of tumorigenic gene modules, CD93, TRIM28, SLC3A2, CBX4, PATL1, and ZNF473 with high intramodular connectivity were assumed as intramodular hub genes in AEG. Conclusion: The weighted gene co-expression network analysis conducted in this study screened out CD93, TRIM28, SLC3A2, CBX4, PATL1, and ZNF473 may act as candidate biomarker in GC and AEG.

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

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.

scholarly journals RBBP4 Enhances Platinum Chemo Resistance in Lung Adenocarcinoma

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.

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

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.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wei Xu ◽  
Jian Xu ◽  
Zhiqiang Wang ◽  
Yuequan Jiang
Keyword(s):  
Esophageal Cancer ◽  
Network Analysis ◽  
Cell Cycle Progression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Future Research ◽  
Hub Genes ◽  
Pathological Staging

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.

scholarly journals Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

scholarly journals Identification of potential prognostic markers for osteosarcoma by integrated analysis

2020 ◽  
Author(s):  
Yuxiang Ge ◽  
Wang Ding ◽  
Chong Bian ◽  
Huijie Gu ◽  
Jun Xu ◽  
...  
Keyword(s):  
Prognostic Markers ◽  
Bone Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Great Promise ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Modules

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

scholarly journals Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

2019 ◽  
Author(s):  
Yunze Liu ◽  
Xiaojie Sun ◽  
Aijun Qu
Keyword(s):  
Drosophila Melanogaster ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Neuronal Remodeling ◽  
Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

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

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.

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

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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