Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer

2017 ◽  
Vol 37 (3) ◽  
pp. 319-325 ◽  
Author(s):  
Cong Zhang ◽  
Qian Sun
Keyword(s):  
Esophageal Cancer ◽  
Network Analysis ◽  
Gene Modules

scholarly journals Gene co-expression network analysis reveals key potential gene modules in utero-vaginal junction associated with duration of fertility trait of breeder hens

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Lantao Gu ◽  
Ruoxi Jing ◽  
Yanzhang Gong ◽  
Mei Yu ◽  
Abdelmotaleb Elokil ◽  
...  
Keyword(s):  
Network Analysis ◽  
Sperm Storage ◽  
Production Performance ◽  
Rt Pcr ◽  
Hub Genes ◽  
Breeder Hens ◽  
Fertility Trait ◽  
Gene Modules ◽  
Highly Correlated ◽  
Insight Into

Abstract The number of days (DN) when hens lay fertile eggs as well as the number of fertile eggs (FN) were produced after a single artificial insemination (AI), including the two duration of fertility (DF) traits. Indeed, they are the key production performance that associates with the production cost of hatching egg when its determination the interval between successive artificial inseminations. However, the relevant genes response for regulating the DF has not been uncovered yet. Therefore, we performed a weighted gene co-expression network analysis (WGCNA) to investigate the insight into co-expression gene modules on DF process in hens. The total mRNA was extracted from the utero-vaginal junction (UVJ, with the sperm storage function in hen’s oviduct which is the biological basis for DF) of 20 hens with several levels of DF traits, and performed transcriptome sequences of mRNA. As a result, three co-expression gene modules were identified to be highly correlated with DF traits. Moreover, the expression changes of top 5 hub genes in each module with DF traits were further confirmed in other 20 hens by RT-PCR. These findings highlighted the co-expression modules and their affiliated genes as playing important roles in the regulation of DF traits.

scholarly journals SO004IDENTIFICATION OF HUB GENES, FOCUSED ON CHEMOKINES AND CHEMOKINE RECEPTORS, ASSOCIATED WITH THE DECLINE OF RENAL FUCTION OF DIABETIC KIDNEY DISEASE BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Songtao Feng ◽  
Bicheng Liu ◽  
Linli Lv ◽  
Gao Yueming ◽  
Di Yin ◽  
...  
Keyword(s):  
Renal Function ◽  
Network Analysis ◽  
Kidney Disease ◽  
T Cell Activation ◽  
Chemokine Receptors ◽  
Diabetic Kidney Disease ◽  
Expression Level ◽  
Hub Genes ◽  
Diabetic Kidney ◽  
Gene Modules

Abstract Background and Aims The fact that activation of the innate immune system and chronic inflammation are closely involved in the pathogenesis of diabetic Kidney disease (DKD). Recent studies have suggested the inflammatory process plays a crucial role in the progression of DKD. Identifying novel inflammatory molecules closely related to the decline of renal function is of significance in diagnosing and predicting the progression of DKD. The weighted gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology method that provide the approach of association between gene modules and clinical traits to find the genes involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DKD from the gene sets associated with the decline of renal function by WGCNA. Method The Gene Expression Omnibus (GEO) database and “Nephroseq” website were searched and transcriptome study from DN biopsies with well-established clinical phenotypic data were selected for analysis. Next, we constructed a weighted gene co-expression network and identified modules negatively correlated with eGFR by WGCNA in the data of glomerular tissue. Functional annotations of the genes in modules negatively correlated with eGFR were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes were obtained. Furthermore, we compared the expression level of hub genes between DKD and normal control and drew ROC curves to determine the diagnosis value to DKD of these genes. Results The microarray-based expression datasets GSE30528 were screened out for analysis, which included glomeruli tissue of 9 cases of DKD and 13 cases of control. This microarray platform represented the transcriptome profile of 12411 well-characterized genes. Using WGCNA, a total of 19 gene modules were identified. Then module eigengene were analyzed for correlation with clinical traits of age, sex, ethnicity and eGFR and the “MEhoneydew1” module showed negative associated with eGFR (r=-0.58). GO functional annotation showed that these 551 genes in the “MEhoneydew1” module mainly enriched in the T cell activation. KEGG annotation showed mainly enriched in chemokine signaling pathway. Except for C3, top 10 hub genes, CCR5, CXCR4, CCR7, CCL5, CXCL8, CCR2, CCR1, CX3CR1, C3AR1 and C3, are all members of chemokines or chemokine receptors. Furthermore, we compared the expression level of these 9 genes between DKD and control, and found that all of these 9 genes increased in the DKD group, and the differences of 6 genes, CCR5, CCR7, CCL5, CCR2, CCR1, C3AR1, were of statistical significance. Linear correlation analysis showed that the expression of these 6 genes was negatively correlated with eGFR, and the ROC curve showed that the area under the curve could reach 0.812∼1.0. Conclusion We identified a panel of 6 hub genes focused on chemokines and chemokine receptors critical for decline of renal function of DKD using WGCNA. These genes may serve as biomarkers for diagnosis/prognosis and as putative novel therapeutic targets for DKD.

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 Weighted Gene Co-expression Network Analysis Reveals Different Immunity but Shared Renal Pathology Between IgA Nephropathy and Lupus Nephritis

2021 ◽  
Vol 12 ◽  
Author(s):  
Ni-Ya Jia ◽  
Xing-Zi Liu ◽  
Zhao Zhang ◽  
Hong Zhang
Keyword(s):  
Gene Expression ◽  
Cell Death ◽  
Network Analysis ◽  
Lupus Nephritis ◽  
Iga Nephropathy ◽  
Enrichment Analysis ◽  
Extracellular Vesicle ◽  
Gene Modules ◽  
Similarities And Differences ◽  
Blue Module

Both IgA nephropathy (IgAN) and lupus nephritis (LN) are immunity-related diseases with a complex, polygenic, and pleiotropic genetic architecture. However, the mechanism by which the genetic variants impart immunity or renal dysfunction remains to be clarified. In this study, using gene expression datasets as a quantitative readout of peripheral blood mononuclear cell (PBMC)- and kidney-based molecular phenotypes, we analyzed the similarities and differences in the patterns of gene expression perturbations associated with the systematic and kidney immunity in IgAN and LN. Original gene expression datasets for PBMC, glomerulus, and tubule from IgAN and systemic lupus erythematosus (SLE) patients as well as corresponding controls were obtained from the Gene Expression Omnibus (GEO) database. The similarities and differences in the expression patterns were detected according to gene differential expression. Weighted gene co-expression network analysis (WGCNA) was used to cluster and screen the co-expressed gene modules. The disease correlations were then identified by cell-specific and functional enrichment analyses. By combining these results with the genotype data, we identified the differentially expressed genes causatively associated with the disease. There was a significant positive correlation with the kidney expression profile, but no significant correlation with PBMC. Three co-expression gene modules were screened by WGCNA and enrichment analysis. Among them, blue module was enriched for glomerulus and podocyte (P &lt; 0.05) and positively correlated with both diseases (P &lt; 0.05), mainly via immune regulatory pathways. Pink module and purple module were enriched for tubular epithelium and correlated with both diseases (P &lt; 0.05) through predominant cell death and extracellular vesicle pathways, respectively. In genome-wide association study (GWAS) enrichment analysis, blue module was identified as the high-risk gene module that distinguishes LN from SLE and contains PSMB8 and PSMB9, the susceptibility genes for IgAN. In conclusion, IgAN and LN showed different systematic immunity but similarly abnormal immunity in kidney. Immunological pathways may be involved in the glomerulopathy and cell death together with the extracellular vesicle pathway, which may be involved in the tubular injury in both diseases. Blue module may cover the causal susceptibility gene for IgAN and LN.

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

2019 ◽  
Author(s):  
Zheying Zhang ◽  
Na Li ◽  
Qingzu Gao ◽  
Xinlai Qian
Keyword(s):  
Colorectal Cancer ◽  
Developing Countries ◽  
Network Analysis ◽  
Malignant Tumor ◽  
Hub Genes ◽  
Hub Gene ◽  
Chip Data ◽  
Qrt Pcr ◽  
Gene Modules

Background: Colorectal cancer (CRC) is a malignant tumor particularly common in developing countries. In this study, we used Weighted Gene Co-Expression Network Analysis (WGCNA) of chip data and screened hub genes in CRC to find the gene modules specifically correlated with clinical traits. Methods: WGCNA was used to identify the gene modules specifically associated with metastasis in colorectal cancer. Cytoscape software was used to construct a co-expression network. The expression of CYTH1 was determined by qRT-PCR. Results: Based on the predicted co-expression network, we identified that the turquoise module was associated with CRC clinical metastasis traits. Turquoise module genes were analyzed, and we identified the hub gene CYTH1 using Cytoscape software. Additionally, we found CYTH1’s expression was lower in CRC tissue and cells when compared with normal counterparts.

Identifying gene modules of thyroid cancer associated with pathological stage by weighted gene co-expression network analysis

Gene ◽  
2019 ◽  
Vol 704 ◽  
pp. 142-148 ◽  
Author(s):  
Xiaozhun Tang ◽  
Xiaoliang Huang ◽  
Duoping Wang ◽  
Ruogu Yan ◽  
Fen Lu ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Network Analysis ◽  
Pathological Stage ◽  
Gene Modules

P0971IDENTIFICATION OF HUB GENES ASSOCIATED WITH THE DEPOSITION OF EXTRACELLULAR MATRIX AND SPECIFIC FOR DIABETIC NEPHROPATHY BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Songtao Feng ◽  
Linli Lv ◽  
Gao Yueming ◽  
Cao Jingyuan ◽  
Di Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Expression Level ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Screening ◽  
Gene Modules ◽  
Geo Database

Abstract Background and Aims Diabetic nephropathy (DN) and its most severe manifestation, end-stage renal disease (ESRD), remains one of the leading causes of reduced lifespan in people with diabetes. Identifying novel molecules that are involved in the pathogenesis of DN has both diagnostic and therapeutic implications. The gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology approach that provide the approach of association between gene modules and clinical traits to find the module involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DN from the aspect of whole gene transcripts analysis. Method Various types of chronic kidney diseases (CKD), including DN, microarray-based mRNA gene expression data, listed in the Gene Expression Omnibus (GEO) database, were analyzed. Next, we constructed a weighted gene co-expression network and identified modules distinguishing DN from normal or other types of CKD by WGCNA. Functional annotations of the genes in modules specialized for DN were analyzed by Gene Ontology (GO) enrichment analysis. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes specific for DN were obtained. Furthermore, we drew ROC curves to determine the diagnosis and differential diagnosis value to DN of hub genes. Finally, another study of microarray in the GEO database was selected to verify the expression level of hub genes and in the “Nephroseq” database, the correlation between the gene expression level and eGFR was analyzed. Results “GSE99339”, glomerular tissue microarray in 187 patients with a total of 10947 genes, was selected for analysis. After excluding the inappropriate cases, a total of 179 specimens were analyzed, including 14 cases of DN, 22 cases of focal segmental glomerulosclerosis (FSGS), 15 cases of hypertensive nephropathy (HT), 26 cases of IgA nephropathy (IgAN), 13 cases of minimal change disease (MCD), 21 cases of membranous nephropathy (MGN), 23 cases of rapidly progressive glomerulonephritis (RPGN), 30 cases of lupus nephritis (LN) and 14 cases of kidney tissue adjacent to tumor. Co-expression network analysis by WGCNA identified 23 distinct gene modules of the total 10947 genes and revealed “MEsaddlegreen” module was strongly correlated with DN (r=0,54), but not with other groups. GO functional annotation showed that these 64 genes in the “MEsaddlegreen” module mainly enriched in the deposition of extracellular matrix, which represents the specific and diagnostic pathophysiological process of DN. Further PPI and hub gene screening analysis revealed that LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD can be served as hub genes, which had been proved to play an important role in the deposition of extracellular matrix. Furthermore, we found that the expression of hub genes was the highest in DN group and for the diagnosis value of DN by each gene, the area under the ROC curve is about 0.75∼0.95. The external verification of another study showed that compared with the normal control group, the expression of these hub genes was the highest in the DN group, and their expression level was negatively correlated with eGFR. Conclusion Using WGCNA and further bioinformatics approach, we identified six hub genes that appear to be identical to DN development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.

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