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      The SUMO E3 ligase CBX4 is identified as a poor prognostic marker of gastric cancer through multipronged OMIC analyses

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          Abstract

          Gastric cancer (GC) is one of the most common malignancies, with an ever-increasing incidence and high mortality rate. Chromobox4 (CBX4), also named hPC2, is a small ubiquitin-related modifier (SUMO) E3 ligase. Previous studies have found that high CBX4 expression is associated with tumor size, pathologic differentiation and decreased patient survival in hepatocellular carcinoma (HCC). However, the expression and prognostic value of CBX4 in GC have not been clarified. In our study, ONCOMINE, UALCAN, Kaplan-Meier Plotter, cBioPortal, DAVID 6.8 and TIMER were utilized. RT-PCR, immunohistochemistry (IHC), Western blot, CCK-8 assay, cell apoptosis assay, cell cycle assay were used to further verify in GC tissue samples or cell line. The transcriptional and protein level of CBX4 in GC tissues was found significantly elevated and a significant association between the expression of CBX4 and clinicopathological parameters was found in GC patients. Low expression of CBX4 in GC patients were correlated with a significantly improved prognosis. The functions of CBX4 are primarily related to the stem cell pluripotency signaling pathway, Hippo signaling pathway, HTLV-I infection, Notch signaling pathway, and N-glycan biosynthesis. Our results may provide novel insights for the selection of therapeutic targets and prognostic biomarkers for GC.

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          Most cited references35

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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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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                Author and article information

                Contributors
                Journal
                Genes Dis
                Genes Dis
                Genes & Diseases
                Chongqing Medical University
                2352-4820
                2352-3042
                01 September 2020
                November 2021
                01 September 2020
                : 8
                : 6
                : 827-837
                Affiliations
                [1]Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, PR China
                Author notes
                []Corresponding author. Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai, Jiao Tong University, No. 160 Pujian Avenue, Shanghai, 200127, PR China. shuliangzhao@ 123456126.com
                [∗∗ ]Corresponding author. Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Shanghai Institute of Digestive Disease, Renji Hospital, School of Medicine, Shanghai, Jiao Tong University, No. 160 Pujian Avenue, Shanghai, 200127, PR China. caozj_renji@ 123456163.com
                [1]

                Equal contribution.

                Article
                S2352-3042(20)30115-X
                10.1016/j.gendis.2020.08.010
                8427259
                34522711
                fa5af81c-33e5-4ccf-a565-8ccb30d659e2
                © 2020 Chongqing Medical University. Production and hosting by Elsevier B.V.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 24 June 2020
                : 13 August 2020
                : 24 August 2020
                Categories
                Full Length Article

                bioinformatics analysis,chromobox4 (cbx4),gastric cancer,prognostic biomarker,therapeutic target

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