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Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.

ABSTRACT: The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC.A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic network was assigned a weight value, and then candidate genes were obtained based on the weight distribution. Eventually, pathway enrichment analysis for candidate genes was performed.In this work, a total of 330 DEGs were identified between CC and normal controls. From the pathogenic network, 2 intensely connected clusters were extracted, and a total of 52 candidate genes were detected under the weight values greater than 0.10. Among these candidate genes, VIM had the highest weight value. Moreover, candidate genes MMP1, CDC45, and CAT were, respectively, enriched in pathway in cancer, cell cycle, and methane metabolism.Candidate pathogenic genes including MMP1, CDC45, CAT, and VIM might be involved in the pathogenesis of CC. We believe that our results can provide theoretical guidelines for future clinical application.

SUBMITTER: Zhang YX

PROVIDER: S-EPMC4789371 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.

Zhang Yun-Xia YX Zhao Yan-Li YL

Computational and mathematical methods in medicine 20160229

<h4>Purpose</h4>The objective of our study was to predicate candidate genes in cervical cancer (CC) using a network-based strategy and to understand the pathogenic process of CC.<h4>Methods</h4>A pathogenic network of CC was extracted based on known pathogenic genes (seed genes) and differentially expressed genes (DEGs) between CC and normal controls. Subsequently, cluster analysis was performed to identify the subnetworks in the pathogenic network using ClusterONE. Each gene in the pathogenic n ...[more]

PMID: 27034707

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Project description:PurposesCervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.ResultsThe mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.ConclusionWe have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

Project description:BackgroundThe 5-year survival rate of patients with pancreatic ductal adenocarcinoma (PDAC) is around 5% due to the fact that the majority of patients present with advanced disease that is treatment resistant. Familial pancreatic cancer (FPC) is a rare disorder that is defined as a family with at least two affected first degree relatives, with an estimated incidence of 4%-10%. The genetic basis is unknown in the majority of families although around 10%-13% of families carry germline mutations in known genes associated with hereditary cancer and pancreatitis syndromes.MethodsPanel sequencing was performed of 35 genes associated with hereditary cancer in 43 PDAC cases from families with an apparent hereditary pancreatic cancer syndrome.FindingsPathogenic variants were identified in 19% (5/26) of PDAC cases from pure FPC families in the genes MLH1, CDKN2A, POLQ and FANCM. Low frequency potentially pathogenic VUS were also identified in 35% (9/26) of PDAC cases from FPC families in the genes FANCC, MLH1, PMS2, CFTR, APC and MUTYH. Furthermore, an important proportion of PDAC cases harboured more than one pathogenic, likely pathogenic or potentially pathogenic VUS, highlighting the multigene phenotype of FPC.InterpretationThe genetic basis of familial or hereditary pancreatic cancer can be explained in 21% of families by previously described hereditary cancer genes. Low frequency variants in other DNA repair genes are also present in 35% of families which may contribute to the risk of pancreatic cancer development.FundingThis study was funded by the Instituto de Salud Carlos III (Plan Estatal de I + D + i 2013-2016): ISCIII (PI09/02221, PI12/01635, PI15/02101 and PI18/1034) and co-financed by the European Development Regional Fund ''A way to achieve Europe'' (ERDF), the Biomedical Research Network in Cancer: CIBERONC (CB16/12/00446), Red Temática de investigación cooperativa en cáncer: RTICC (RD12/0036/0073) and La Asociación Española contra el Cáncer: AECC (Grupos Coordinados Estables 2016).

Dataset Information

Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.

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Pathogenic Network Analysis Predicts Candidate Genes for Cervical Cancer.

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