Dataset Information

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

ABSTRACT: Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

SUBMITTER: Jin N

PROVIDER: S-EPMC4477367 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

Jin Nana N Wu Hao H Miao Zhengqiang Z Huang Yan Y Hu Yongfei Y Bi Xiaoman X Wu Deng D Qian Kun K Wang Liqiang L Wang Changliang C Wang Hongwei H Li Kongning K Li Xia X Wang Dong D

Scientific reports 20150623

Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be br ...[more]

PMID: 26099452

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Project description:BackgroundSudden cardiac death (SCD) accounts for a large proportion of the total deaths across different age groups. Although numerous candidate genes related to SCD have been identified by genetic association studies and genome wide association studies (GWAS), the molecular mechanisms underlying SCD are still unclear, and the biological functions and interactions of these genes remain obscure. To clarify this issue, we performed a comprehensive and systematic analysis of SCD-related genes by a network and pathway-based approach.MethodsBy screening the publications deposited in the PubMed and Gene-Cloud Biotechnology Information (GCBI) databases, we collected the genes genetically associated with SCD, which were referred to as the SCD-related gene set (SCDgset). To analyze the biological processes and biochemical pathways of the SCD-related genes, functional analysis was performed. To explore interlinks and interactions of the enriched pathways, pathway crosstalk analysis was implemented. To construct SCD-specific molecular networks, Markov cluster algorithm and Steiner minimal tree algorithm were employed.ResultsWe collected 257 genes that were reported to be associated with SCD and summarized them in the SCDgset. Most of the biological processes and biochemical pathways were related to heart diseases, while some of the biological functions may be noncardiac causes of SCD. The enriched pathways could be roughly grouped into two modules. One module was related to calcium signaling pathway and the other was related to MAPK pathway. Moreover, two different SCD-specific molecular networks were inferred, and 23 novel genes potentially associated with SCD were also identified.ConclusionsIn summary, by means of a network and pathway-based methodology, we explored the pathogenetic mechanism underlying SCD. Our results provide valuable information in understanding the pathogenesis of SCD and include novel biomarkers for diagnosing potential patients with heart diseases; these may help in reducing the corresponding risks and even aid in preventing SCD.

Project description:Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox's proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by L(2) or L(1). This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.

Dataset Information

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

Publications

Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer.

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