Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis.

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, ge...

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Publicado en:BioMed Research International pp. 1 - 13
Autores principales: Chong, Xinyu, Peng, Rui, Sun, Yan, Zhang, Luyu, Zhang, Zheng
Formato: research tables/charts Journal Article
Publicado: Wiley-Blackwell 9/21/2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 9/21/2020
      pid: 480
      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1155/2020/7658230
        145990475
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        atl: Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis.
      aug:
        au:
          Chong, Xinyu
          Peng, Rui
          Sun, Yan
          Zhang, Luyu
          Zhang, Zheng
        affil: Department of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China
      sug:
        subj:
          Gene Expression
          Stomach Neoplasms
          Bioinformatics
          Genetic Markers
          Genes
          Human
          Ontologies
          Kaplan-Meier Estimator
          Signal Transduction
          Proteins
          Fibronectins
          Metalloproteins
          Phosphoproteins
          Apolipoproteins
          Glycoproteins
          Genetics
      ab: Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
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      ougenre: Article
    language: English
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