Dataset Information

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.

ABSTRACT: BACKGROUND:Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma. METHOD:Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients. RESULTS:We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156). CONCLUSION:We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

SUBMITTER: Xu P

PROVIDER: S-EPMC6211550 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.

Xu Pengfei P Yang Jian J Liu Junhui J Yang Xue X Liao Jianming J Yuan Fanen F Xu Yang Y Liu Baohui B Chen Qianxue Q

BMC medical genomics 20181101 1

<h4>Background</h4>Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.<h4>Method</h4>Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway ...[more]

PMID: 30382873

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Project description:BackgroundGlioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene.MethodsTwo GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis.ResultsGSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3.ConclusionFUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Project description:BackgroundGlioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.ResultsWe extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = - 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e-02)), P4HA2 expression is negatively correlated with tumor purity (cor =-0.279, p = 6.06e-09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.ConclusionWe used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.

Project description:Osteoarthritis (OA) is one of the most prevalent causes of joint disease. However, the pathological mechanisms of OA have remained to be completely elucidated, and further investigation into the underlying mechanisms of OA development and the identification of novel therapeutic targets are urgently required. In the present study, the dataset GSE114007 was downloaded from the Gene Expression Omnibus database. Based on weighted gene co-expression network analysis (WGCNA) and the identification of differentially expressed genes (DEGs), the microarray data were further analyzed to identify hub genes, key transcription factors (TFs) and pivotal signaling pathways involved in the pathogenesis of OA. A total of 1,898 genes were identified to be differentially expressed between OA samples and normal samples. Based on WGCNA, the present study identified 5 hub modules closely associated with OA, and the potential key TFs for hub modules were further explored based on CisTargetX. The results demonstrated that B-Cell Lymphoma 6, Myelin Gene Expression Factor 2, Activating Transcription Factor 3, CCAAT Enhancer Binding Protein ?, Nuclear Factor Interleukin-3-Regulated, FOS Like Antigen-2, FOS-Like Antigen-1, Fos Proto-Oncogene, JunD Proto-Oncogene, Transcription Factor CP2 Like 1, RELA proto-oncogene NF-kB subunit, SRY-box transcription factor 3, V-Ets Avian Erythroblastosis Virus E26 Oncogene Homolog 2, Interferon Regulatory Factor 4 and REL proto-oncogene, NF-kB subunit were the potential key TFs. In addition, osteoclast differentiation, FoxO, MAPK and PI3K/Akt signaling pathways were revealed to be imperative for the pathogenesis of OA, as these 4 pivotal signaling pathways were observed to be tightly linked through 4 key TFs Fos Proto-Oncogene, JUN, JunD Proto-Oncogene and MYC, and 4 DEGs Vascular Endothelial Growth Factor A, Growth Arrest and DNA Damage Inducible ?, Growth Arrest and DNA Damage Inducible ? and Cyclin D1. The present study identified a set of potential key genes and signaling pathways, and provided an important opportunity to advance the current understanding of OA.

Project description:Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

Dataset Information

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.

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Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis.

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