Browse
Submit Data
Databases
API
Help

Dataset Information

28 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder.

ABSTRACT: Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.

SUBMITTER: Zhang ZQ

PROVIDER: S-EPMC8010671 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis.

Project description:AIM:Cholangiocarcinoma (CCA) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common primary tumor of the liver. However, the pivotal roles of molecular biomarkers in oncogenesis of CCA are unclear. Therefore, we aim to explore the underlying mechanisms of progression and screen for novel prognostic biomarkers and treatment targets. METHOD:The data of mRNA sequencing and clinical information of CCA patients in The Cancer Genome Atlas was analyzed by weighted gene coexpression network analysis (WGCNA). Modules and clinical traits were constructed according to Pearson's correlation analysis, and Gene Ontology and pathway enrichment analysis were applied. Hub genes of these modules were screened by intramodule analysis; Cytoscape with Search Tool for the Retrieval of Interacting Genes was utilized to visualize protein-protein interaction of these modules; hub genes of these modules were validated afterwards. Furthermore, the significance of these genes was confirmed by survival analysis. RESULTS:Genes MRPS18A, CST1, and SCP2 were identified as candidate genes in the module, which was associated with clinical traits including pathological stage, histological grade, and liver function and which also affected overall survival of CCA patients. Nineteen hub genes were analyzed together and were associated with progression and prognosis of CCA. Survival analyses found that several of the multiple genes could serve as biomarkers to stratify CCA patients into low- and high-risk groups. CONCLUSION:These candidate genes could be involved in progression of CCA, which could serve as novel prognostic markers and treatment targets. Moreover, most of them were first reported in CCA and deserve further research.

| S-EPMC6899837 | biostudies-literature

Weighted Gene Coexpression Network Analysis Identifies Key Genes and Pathways Associated with Idiopathic Pulmonary Fibrosis.

Project description:BACKGROUND Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis. MATERIAL AND METHODS Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Connectivity Map (CMAP) was used to identify gene-expression signatures associated with IPF. Weighted gene coexpression network analysis (WGCNA) was used to explore the functional modules involved in the pathogenesis of IPF. RESULTS A total of 191 patients with IPF and 101 normal controls from six genome-wide expression datasets were included. CMAP predicted several small molecular agents as potential gene targets in IPF. Several functional modules were detected that showed the highest correlation with IPF, including an extracellular matrix (ECM) component, and a myeloid leukocyte migration and activation component involved in the immune response. Hub genes were identified in the key functional modules that might have a role in the progression of IPF. CONCLUSIONS WGCNA was used to identify functional modules and hub genes involved in the pathogenesis of IPF.

| S-EPMC6582683 | biostudies-literature

Identification of Hub Genes and Key Pathways Associated With Bipolar Disorder Based on Weighted Gene Co-expression Network Analysis.

Project description:Bipolar disorder (BD) is a complex mental disorder with high mortality and disability rates worldwide; however, research on its pathogenesis and diagnostic methods remains limited. This study aimed to elucidate potential candidate hub genes and key pathways related to BD in a pre-frontal cortex sample. Raw gene expression profile files of GSE53987, including 36 samples, were obtained from the gene expression omnibus (GEO) database. After data pre-processing, 10,094 genes were selected for weighted gene co-expression network analysis (WGCNA). After dividing highly related genes into 19 modules, we found that the pink, midnight blue, and brown modules were highly correlated with BD. Functional annotation and pathway enrichment analysis for modules, which indicated some key pathways, were conducted based on the Enrichr database. One of the most remarkable significant pathways is the Hippo signaling pathway and its positive transcriptional regulation. Finally, 30 hub genes were identified in three modules. Hub genes with a high degree of connectivity in the PPI network are significantly enriched in positive regulation of transcription. In addition, the hub genes were validated based on another dataset (GSE12649). Taken together, the identification of these 30 hub genes and enrichment pathways might have important clinical implications for BD treatment and diagnosis.

| S-EPMC6710482 | biostudies-literature

Weighted Gene Coexpression Network Analysis Uncovers Critical Genes and Pathways for Multiple Brain Regions in Parkinson's Disease.

Project description:ObjectiveIn this study, we aimed to identify critical genes and pathways for multiple brain regions in Parkinson's disease (PD) by weighted gene coexpression network analysis (WGCNA).MethodsFrom the GEO database, differentially expressed genes (DEGs) were separately identified between the substantia nigra, putamen, prefrontal cortex area, and cingulate gyrus of PD and normal samples with the screening criteria of p value < 0.05 and ∣log2fold change (FC) | >0.585. Then, a coexpression network was presented by the WGCNA package. Gene modules related to PD were constructed. Then, PD-related DEGs were used for construction of PPI networks. Hub genes were determined by the cytoHubba plug-in. Functional enrichment analysis was then performed.ResultsDEGs were identified for the substantia nigra (17 upregulated and 52 downregulated genes), putamen (317 upregulated and 317 downregulated genes), prefrontal cortex area (39 upregulated and 72 downregulated genes), and cingulate gyrus (116 upregulated and 292 downregulated genes) of PD compared to normal samples. Gene modules were separately built for the four brain regions of PD. PPI networks revealed hub genes for the substantia nigra (SLC6A3, SLC18A2, and TH), putamen (BMP4 and SNAP25), prefrontal cortex area (SNAP25), and cingulate gyrus (CTGF, CDH1, and COL5A1) of PD. These DEGs in multiple brain regions were involved in distinct biological functions and pathways. GSEA showed that these DEGs were all significantly enriched in electron transport chain, proteasome degradation, and synaptic vesicle pathway.ConclusionOur findings revealed critical genes and pathways for multiple brain regions in PD, which deepened the understanding of PD-related molecular mechanisms.

| S-EPMC7984900 | biostudies-literature

Weighted Gene Coexpression Network Analysis Identified MicroRNA Coexpression Modules and Related Pathways in Type 2 Diabetes Mellitus.

Project description:ObjectiveType 2 diabetes mellitus (T2DM) is a metabolic disease with high incidence, which has seriously affected human life and health. MicroRNA, a short-chain noncoding RNA, plays an important role in T2DM. Identification of meaningful microRNA modules and the role of microRNAs provide a basis for searching potential biomarkers of T2DM.Materials and methodsIn this study, three newly diagnosed patients with T2DM and three controls were selected for Whole Peripheral Blood RNA Sequencing to establish a microRNA library. Weighted gene coexpression network analysis (WGCNA) was applied to construct coexpression modules and to detect the trait-related microRNA modules; then, KEGG enrichment analysis was performed to predict the biological function of the interest modules, and candidate hub microRNAs were screened out by the value of module membership (MM) and protein-protein interaction (PPI) network.ResultFour microRNA modules (blue, brown, magenta, and turquoise) were highly associated with the T2DM; the number of miRNAs in these modules ranged from 41 to 469. The Fc gamma R-mediated phagocytosis pathway, Rap1 signaling pathway, MAPK signaling pathway, and Lysosome pathway were common pathways in three of the four modules. RPS27A, UBC, and RAC1 were the top three proteins in our study; their corresponding RNAs were miR-1271-5p, miR-130a-3p, miR-130b-3p, and miR-574-3p.ConclusionIn summary, this study identified blood miRNAs in human T2DM using RNA sequencing. The findings may be the foundation for understanding the potential role of miRNAs in T2DM.

| S-EPMC6935443 | biostudies-literature

Identification of LINC02454-related key pathways and genes in papillary thyroid cancer by weighted gene coexpression network analysis (WGCNA).

Project description:BackgroundOur previous study demonstrated that long intergenic noncoding RNA 02454 (LINC02454) may act as an oncogene to promote the proliferation and inhibit the apoptosis of papillary thyroid cancer (PTC) cells. This study was designed to investigate the mechanisms whereby LINC02454 is related to PTC tumorigenesis.MethodsThyroid cancer RNA sequence data were obtained from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify modules closely associated with PTC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways, and the maximal clique centrality (MCC) topological method was used to identify the hub genes. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to compare expression levels of key genes between PTC samples and normal samples and explore the prognostic value of key genes. The key genes were further validated with GEO dataset.ResultsThe top 5000 variable genes were investigated, followed by an analysis of 8 modules, and the turquoise module was the most positively correlated with the clinical stage of PTC. KEGG pathway analysis found the top two pathways of the ECM - receptor interaction and MAPK signaling pathway. In addition, five key genes (FN1, LAMB3, ITGA3, SDC4, and IL1RAP) were identified through the MCC algorithm and KEGG analysis. The expression levels of the five key genes were significantly upregulated in thyroid cancer in both TCGA and GEO datasets, and of these five genes, FN1 and ITGA3 were associated with poor disease-free prognosis.ConclusionsOur study identified five key genes and two key pathways associated with LINC02454, which might shed light on the underlying mechanism of LINC02454 action in PTC.

| S-EPMC11367880 | biostudies-literature

Identification of Prognostic Genes in Neuroblastoma in Children by Weighted Gene Coexpression Network Analysis.

Project description:BackgroundNeuroblastoma is a malignant neuroendocrine tumor from the sympathetic nervous system, the most common extracranial tumor in children. Identifying potential prognostic markers of neuroblastoma can provide clues for early diagnosis, recurrence, and treatment.MethodsRNA sequence data and clinical features of 147 neuroblastomas were obtained from the TARGET (Therapeutically Applicable Research to Generate Effective Treatments project) database. Application weighted gene coexpression network analysis (WGCNA) was used to construct a free-scale gene coexpression network, to study the interrelationship between its potential modules and clinical features, and to identify hub genes in the module. We performed Lasso regression and Cox regression analyses to identify the three most important genes and develop a new prognostic model. Data from the GSE85047 cohort verified the predictive accuracy of the prognostic model.Results14 coexpression modules were constructed using WGCNA. Brown coexpression modules were found to be significantly associated with disease survival status. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analyses using the Cox proportional hazards regression model. Finally, we constructed a three-gene prognostic model: risk score = (0.003812659∗CKB) + (-0.152376975∗expDST) + (0.032032815∗expDUT). The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (P=1.225e - 06). The risk model was also regarded as an independent predictor of prognosis (HR = 1.632; 95% CI = 1.391-1.934; P < 0.001).ConclusionOur study constructed a neuroblastoma coexpressing gene module and identified a prognostic potential risk model for prognosis in neuroblastoma.

| S-EPMC8331277 | biostudies-literature

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis.

Project description:BackgroundThe pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.MethodsTwo human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.ResultsIn this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.ConclusionsThese results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

| S-EPMC8642897 | biostudies-literature

Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis.

Project description:The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds.

| S-EPMC9256319 | biostudies-literature

Weighted gene coexpression network analysis identifies hub genes related to KRAS mutant lung adenocarcinoma.

Project description:The aim of current study was to use Weighted Gene Coexpression Network Analysis (WGCNA) to identify hub genes related to the incidence and prognosis of KRAS mutant (MT) lung adenocarcinoma (LUAD).We involved 184 stage IIB to IV LUAD samples and 59 normal lung tissue samples from The Cancer Genome Atlas (TCGA) database. The R package "limma" was used to identify differentially expressed genes (DEGs). WGCNA and survival analyses were performed by R packages "WGCNA" and "survival," respectively. The functional analyses were performed by R package "clusterProfiler" and GSEA software. Network construction and MCODE analysis were performed by Cytoscape_v3.6.1.Totally 2590 KRAS MT specific DEGs were found between LUAD and normal lung tissues, and 10 WGCNA modules were identified. Functional analysis of the key module showed the ribosome biogenesis related terms were enriched. We observed the expression of 8 genes were positively correlated to the worse survival of KRAS MT LUAD patients, the 7 of them were validated by Kaplan-Meier plotter database (kmplot.com/) (thymosin Beta 10 [TMSB10], ribosomal Protein S16 [RPS16], mitochondrial ribosomal protein L27 [MRPL27], cytochrome c oxidase subunit 6A1 [COX6A1], HCLS1-associated protein X-1 [HAX1], ribosomal protein L38 [RPL38], and ATP Synthase Membrane Subunit DAPIT [ATP5MD]). The GSEA analysis found mTOR and STK33 pathways were upregulated in KRAS MT LUAD (P < .05, false discovery rate [FDR] < 0.25).In summary, our study firstly used WGCNA to identify hub genes in the development of KRAS MT LUAD. The identified prognostic factors would be potential biomarkers in clinical use. Further molecular studies are required to confirm the mechanism of those genes in KRAS MT LUAD.

| S-EPMC7593058 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data