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

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.

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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

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa139.so004 ◽

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.

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

10.7287/peerj.preprints.27667v1 ◽

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.

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

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p0971 ◽

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

10.21203/rs.2.24360/v1 ◽

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.

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Gene Co-Expression Network Analysis Identifies Vitamin D-Associated Gene Modules in Adult Normal Rectal Epithelium Following Supplementation

Frontiers in Genetics ◽

10.3389/fgene.2021.783970 ◽

2022 ◽

Vol 12 ◽

Author(s):

James P. Blackmur ◽

Peter G. Vaughan-Shaw ◽

Kevin Donnelly ◽

Bradley T. Harris ◽

Victoria Svinti ◽

...

Keyword(s):

Vitamin D ◽

Network Analysis ◽

Expression Patterns ◽

Rectal Mucosa ◽

Vitamin D Supplementation ◽

Plasma Vitamin ◽

Target Tissue ◽

Hub Genes ◽

Gene Modules

Colorectal cancer (CRC) is a common, multifactorial disease. While observational studies have identified an association between lower vitamin D and higher CRC risk, supplementation trials have been inconclusive and the mechanisms by which vitamin D may modulate CRC risk are not well understood. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify modules present after vitamin D supplementation (when plasma vitamin D level was sufficient) which were absent before supplementation, and then to identify influential genes in those modules. The transcriptome from normal rectal mucosa biopsies of 49 individuals free from CRC were assessed before and after 12 weeks of 3200IU/day vitamin D (Fultium-D3) supplementation using paired-end total RNAseq. While the effects on expression patterns following vitamin D supplementation were subtle, WGCNA identified highly correlated genes forming gene modules. Four of the 17 modules identified in the post-vitamin D network were not preserved in the pre-vitamin D network, shedding new light on the biochemical impact of supplementation. These modules were enriched for GO terms related to the immune system, hormone metabolism, cell growth and RNA metabolism. Across the four treatment-associated modules, 51 hub genes were identified, with enrichment of 40 different transcription factor motifs in promoter regions of those genes, including VDR:RXR. Six of the hub genes were nominally differentially expressed in studies of vitamin D effects on adult normal mucosa organoids: LCN2, HLA-C, AIF1L, PTPRU, PDE4B and IFI6. By taking a gene-correlation network approach, we have described vitamin D induced changes to gene modules in normal human rectal epithelium in vivo, the target tissue from which CRC develops.

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Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.580190 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jingni Wu ◽

Xiaomeng Xia ◽

Ye Hu ◽

Xiaoling Fang ◽

Sandra Orsulic

Keyword(s):

Network Analysis ◽

Topological Analysis ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Gene Set Enrichment Analysis ◽

Messenger Rnas ◽

Hub Genes ◽

Protein Protein Interaction ◽

Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

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Identifying the hub genes for Duchenne muscular dystrophy and Becker muscular dystrophy by weighted correlation network analysis

BMC Genomic Data ◽

10.1186/s12863-021-01014-w ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Junjie Wang ◽

Qin Fan ◽

Tengbo Yu ◽

Yingze Zhang

Keyword(s):

Gene Expression ◽

Duchenne Muscular Dystrophy ◽

Network Analysis ◽

Muscular Dystrophy ◽

Becker Muscular Dystrophy ◽

Correlation Network ◽

Hub Genes ◽

Highly Correlated ◽

And Control ◽

Weighted Correlation

Abstract Background The goal of this study is to identify the hub genes for Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) via weighted correlation network analysis (WGCNA). Methods The gene expression profile of vastus lateralis biopsy samples obtained in 17 patients with DMD, 11 patients with BMD and 6 healthy individuals was downloaded from the Gene Expression Omnibus (GEO) database (GSE109178). After obtaining different expressed genes (DEGs) via GEO2R, WGCNA was conducted using R package, modules and genes that highly associated with DMD, BMD, and their age or pathology were screened. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis and protein–protein interaction (PPI) network analysis were also conducted. Hub genes and highly correlated clustered genes were identified using Search Tool for the Retrieval of Interacting Genes (STRING) and Cystoscape software. Results One thousand four hundred seventy DEGs were identified between DMD and control, with 1281 upregulated and 189 downregulated DEGs. Four hundred and twenty DEGs were found between BMD and control, with 157 upregulated and 263 upregulated DEGs. Fourteen modules with different colors were identified for DMD vs control, and 7 modules with different colors were identified for BMD vs control. Ten hub genes were summarized for DMD and BMD respectively, 5 hub genes were summarized for BMD age, 5 and 3 highly correlated clustered genes were summarized for DMD age and BMD pathology, respectively. In addition, 20 GO enrichments were found to be involved in DMD, 3 GO enrichments were found to be involved in BMD, 3 GO enrichments were found to be involved in BMD age. Conclusion In DMD, several hub genes were identified: C3AR1, TLR7, IRF8, FYB and CD33(immune and inflammation associated genes), TYROBP, PLEK, AIF1(actin reorganization associated genes), LAPTM5 and NT5E(cell death and arterial calcification associated genes, respectively). In BMD, a number of hub genes were identified: LOX, ELN, PLEK, IKZF1, CTSK, THBS2, ADAMTS2, COL5A1(extracellular matrix associated genes), BCL2L1 and CDK2(cell cycle associated genes).

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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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Weighted gene correlation network analysis reveals novel biomarkers associated with mesenchymal stromal cell differentiation in early phase

PeerJ ◽

10.7717/peerj.8907 ◽

2020 ◽

Vol 8 ◽

pp. e8907 ◽

Cited By ~ 2

Author(s):

Bin Xiao ◽

Guozhu Wang ◽

Weiwei Li

Keyword(s):

Bone Marrow ◽

Network Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Ras Signaling ◽

High Morbidity ◽

Hub Genes ◽

Data Set ◽

Regulatory Pathways ◽

Gene Modules

Osteoporosis is a major public health problem that is associated with high morbidity and mortality, and its prevalence is increasing as the world’s population ages. Therefore, understanding the molecular basis of the disease is becoming a high priority. In this regard, studies have shown that an imbalance in adipogenic and osteogenic differentiation of bone marrow mesenchymal stem cells (MSCs) is associated with osteoporosis. In this study, we conducted a Weighted Gene Co-Expression Network Analysis to identify gene modules associated with the differentiation of bone marrow MSCs. Gene Ontology and Kyoto Encyclopedia of Genes and Genome enrichment analysis showed that the most significant module, the brown module, was enriched with genes involved in cell cycle regulation, which is in line with the initial results published using these data. In addition, the Cytoscape platform was used to identify important hub genes and lncRNAs correlated with the gene modules. Furthermore, differential gene expression analysis identified 157 and 40 genes that were upregulated and downregulated, respectively, after 3 h of MSCs differentiation. Interestingly, regulatory network analysis, and comparison of the differentially expressed genes with those in the brown module identified potential novel biomarker genes, including two transcription factors (ZNF740, FOS) and two hub genes (FOXQ1, SGK1), which were further validated for differential expression in another data set of differentiation of MSCs. Finally, Gene Set Enrichment Analysis suggested that the two most important candidate hub genes are involved in regulatory pathways, such as the JAK-STAT and RAS signaling pathways. In summary, we have revealed new molecular mechanisms of MSCs differentiation and identified novel genes that could be used as potential therapeutic targets for the treatment of osteoporosis.

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