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      Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis

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          Abstract

          Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC.

          Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.

          Results: We identified nine hub genes ( TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals.

          Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC.

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          The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure, which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent receiver operating characteristic (ROC) curves. Semiparametric estimation methods appropriate for both proportional and nonproportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
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            A measure of betweenness centrality based on random walks

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              Genome-wide analysis of non-coding regulatory mutations in cancer

              Cancer primarily develops due to somatic alterations in the genome. Advances in sequencing have enabled large-scale sequencing studies across many tumor types, emphasizing discovery of alterations in protein-coding genes. However, the protein-coding exome comprises less than 2% of the human genome. Here, we analyze complete genome sequences of 863 human tumors from The Cancer Genome Atlas and other sources to systematically identify non-coding regions that are recurrently mutated in cancer. We utilize novel frequency and sequence-based approaches to comprehensively scan the genome for non-coding mutations with potential regulatory impact. We identified recurrent mutations in regulatory elements upstream of PLEKHS1, WDR74, and SDHD, as well as previously identified mutations in the TERT promoter. SDHD promoter mutations are frequent in melanoma and associated with reduced gene expression and poor patient prognosis. The non-protein-coding cancer genome remains widely unexplored and our findings represent a step towards targeting the entire genome for clinical purposes.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                17 July 2018
                2018
                : 9
                : 265
                Affiliations
                [1] 1Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine , Beijing, China
                [2] 2Evidence Based Medicine Center, School of Basic Medical Science, Lanzhou University , Lanzhou, China
                [3] 3Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China
                [4] 4Institute of Modern Physics, Chinese Academy of Sciences , Lanzhou, China
                Author notes

                Edited by: Alfredo Pulvirenti, Università degli Studi di Catania, Italy

                Reviewed by: Matteo Giulietti, Università Politecnica delle Marche, Italy; Jianbo Pan, Johns Hopkins Medicine, United States

                *Correspondence: Jiarui Wu, exogamy@ 123456163.com

                This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2018.00265
                6056647
                30065754
                486f7c49-3c77-4436-b6d2-7f00bfc423e4
                Copyright © 2018 Liu, Wu, Zhang, Bing, Tian, Ni, Zhang, Meng and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 May 2018
                : 02 July 2018
                Page count
                Figures: 7, Tables: 2, Equations: 0, References: 116, Pages: 14, Words: 0
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81473547
                Award ID: 81673829
                Categories
                Genetics
                Original Research

                Genetics
                gastric cancer,bioinformatics,differentially expressed genes,survival,biomarker,geo,tcga
                Genetics
                gastric cancer, bioinformatics, differentially expressed genes, survival, biomarker, geo, tcga

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