Dataset Information

Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.

ABSTRACT: Background:Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized. Methods:GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan-Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed. Results:Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle. Conclusion:BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

SUBMITTER: Liu J

PROVIDER: S-EPMC7031962 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.

Liu JinHui J Feng Mingming M Li SiYue S Nie Sipei S Wang Hui H Wu Shan S Qiu Jiangnan J Zhang Jie J Cheng WenJun W Cheng WenJun W

Cancer cell international 20200219

<h4>Background</h4>Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized.<h4>Methods</h4>GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in C ...[more]

PMID: 32099532

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Project description:BackgroundUterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.MethodsTo identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.ResultsThere were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.ConclusionsBased on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.

Project description:Background and objectiveEndometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.Methods and resultsAn mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein-protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p < 0.001) and time-dependent receiver-operating characteristic (ROC) analysis displayed an area under the curve (AUC) of 0.797, 0.734, 0.729, and 0.647 for 1, 3, 5, and 10-year overall survival (OS) prediction, respectively.ConclusionThis study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.

Project description:BackgroundEndometrial cancer (EC) is one of the female malignant tumors. Endometrial cancer predominately affects post-menopausal women. Bioinformatics analysis has been widely applied to screen and analyze genes in linkage to various types of cancer progression.MethodsDownload the gene expression profile from Gene Expression Omnibus (GEO). Calculate raw expression data according to pre-processing procedures. We performed the "limma" R language package to screen DEGs between Endometrial cancer tissue samples and normal uterus tissue samples. Enrichment of the functions and pathways was analyzed by using clusterprofiler. We utilized Search Tool for the Retrieval of Interacting Genes Database (STRING) to assess protein-protein interaction (PPI) information, and then we used plug-in Molecular Complex Detection (MCODE) to screen hub modules of PPI network in Cytoscape. We also performed functional analysis on the genes in the hub module by using clusterprofiler. Next, we utilized the "WGCNA" package in R to establish co-expression network for the DEGs. The Venn diagram was performed to overlap the gene in key module and hub PPI cluster. We validated the key genes in TCGA, GEPIA, UALCAN and Immunohistochemistry staining obtained from The Human Protein Atlas database. And then we did ROC curve analysis by SPSS. Gene set enrichment analysis (GSEA) and mutation analysis were also performed for hub genes.ResultsFunctional and pathway enrichment analysis demonstrated that the upregulated differentially expressed genes (DEGs) were significantly enriched in CXCR chemokine receptor binding, chemokine activity, chemokine receptor binding, G-protein coupled receptor binding, RAGE receptor binding, cytokine activity, microtubule binding, receptor regulator activity and microtubule motor activity, and the down-regulated genes were highly enriched in collagen binding. After using STRING software to construct PPI network, 30 prominent proteins were identified and the first two significant modules were selected. In co-expression network, 5 EC-related modules were identified. Among them, the turquoise module has the highest correlation with the EC. We further analyzed the genes in the PPI and turquoise module, and selected eleven key genes related to EC after validation of TCGA database, GEPIA, UALCAN and immunohistochemistry. Six of them had mutation significance.ConclusionsIn summary, these 11 genes may become new therapy targets for EC treatment.

Dataset Information

Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.

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Identification of molecular markers associated with the progression and prognosis of endometrial cancer: a bioinformatic study.

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