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      Identification of core genes and outcome in gastric cancer using bioinformatics analysis

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          Abstract

          Gastric cancer (GC) is a common malignant neoplasm of gastrointestinal tract. We chose gene expression profile of GSE54129 from GEO database aiming to find key genes during the occurrence and development of GC. 132 samples, including 111 cancer and 21 normal gastric mucosa epitheliums, were included in this analysis. Differentially expressed genes (DEGs) between GC patients and health people were picked out using GEO2R tool, then we performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was utilized to visualize protein-protein interaction (PPI) of these DEGs. There were 971 DEGs, including 468 up-regulated genes enriched in focal adhesion, ECM-receptor interaction and PI3K-Akt signaling pathway, while 503 down-regulated genes enriched in metabolism of xenbiotics and drug by cytochrome P450, chemical carcinogenesis, retinol metabolism and gastric acid secretion. Three important modules were detected from PPI network using MCODE software. Besides, Fifteen hub genes with high degree of connectivity were selected, including BGN, MMP2, COL1A1, and FN1. Moreover, the Kaplan–Meier analysis for overall survival and correlation analysis were applied among those genes. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as BGN, might promote the development of gastric cancer, especially in tumor metastasis. In addition, it could be used as a new biomarker for diagnosis and to guide the combination medicine of gastric cancer.

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          Most cited references 22

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              KEGG: kyoto encyclopedia of genes and genomes.

               M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                19 September 2017
                9 August 2017
                : 8
                : 41
                : 70271-70280
                Affiliations
                1 The Second Clinical Medical College of Nanjing Medical University, Nanjing, Jiangsu, China
                2 Department of Endocrinology, Huai’an First People’s Hospital, Nanjing Medical University, Huai’an, Jiangsu, China
                3 Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
                Author notes
                Correspondence to: Baolin Wang, wang_blin@ 123456163.com
                [*]

                These authors contributed equally to this work and co-first authors

                Article
                20082
                10.18632/oncotarget.20082
                5642553
                Copyright: © 2017 Sun et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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                Research Paper

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