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      Tetraspanin family identified as the central genes detected in gastric cancer using bioinformatics analysis

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          Abstract

          Gastric cancer has become a serious disease in the past decade. It has the second highest mortality rate among the four most common cancer types, leading to ~700,000 mortalities annually. Previous studies have attempted to elucidate the underlying biological mechanisms of gastric cancer. The present study aimed to obtain useful biomarkers and to improve the understanding of gastric cancer mechanisms at the genetic level. The present study used bioinformatics analysis to identify 1,829 differentially expressed genes (DEGs) which were obtained from the GSE54129 dataset. Using protein-protein interaction information from the Search Tool for the Retrieval of Interacting Genes database, disease modules were constructed for gastric cancer using Cytoscape software. In the Gene Ontology analysis of biology processes, upregulated genes were significantly enriched in ‘extracellular matrix organization’, ‘cell adhesion’ and ‘inflammatory response’, whereas downregulated DEGs were significantly enriched in ‘xenobiotic metabolic process’, ‘oxidation-reduction process’ and ‘steroid metabolic process’. During Kyoto Encyclopedia of Genes and Genomes analysis, upregulated DEGs were significantly enriched in ‘extracellular matrix-receptor interaction’, ‘focal adhesion’ and ‘PI3K-Akt signaling pathway’, whereas the downregulated DEGs were significantly enriched in ‘chemical carcinogenesis’, ‘metabolism of xenobiotics by cytochrome P450’ and ‘peroxisome’. The present study additionally identified 10 hub genes from the DEGs: Tumor protein p53 (TP53), C-X-C motif chemokine ligand 8 (CXCL8), tetraspanin 4 (TSPAN4), lysophosphatidic acid receptor 2 (LPAR2), adenylate cyclase 3 (ADCY3), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), neuromedin U (NMU), C-X-C motif chemokine ligand (CXCL12), fos proto-oncogene, AP-1 transcription factor subunit (FOS) and sphingosine-1-phosphate receptor 1 (S1PR1), which have high degrees with other DEGs. The survival analysis revealed that the high expression of ADCY3, LPAR2, S1PR1, TP53 and TSPAN4 was associated with a lower survival rate, whereas high expression of CXCL8, FOS, NMU and PIK3R1 was associated with a higher survival rate. No significant association was identified between CXCL12 and survival rate. Additionally, TSPAN1 and TSPAN8 appeared in the top 100 DEGs. Finally, it was observed that 4 hub genes were highly expressed in gastric cancer tissue compared with para-carcinoma tissue in the 12 patients; the increased TSPAN4 was significant (>5-fold). Tetraspanin family genes may be novel biomarkers of gastric cancer. The findings of the present study may improve the understanding of the molecular mechanisms underlying the development of gastric cancer.

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          Most cited references38

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          p53 functions as a transcription factor involved in cell-cycle control, DNA repair, apoptosis and cellular stress responses. However, besides inducing cell growth arrest and apoptosis, p53 activation also modulates cellular senescence and organismal aging. Senescence is an irreversible cell-cycle arrest that has a crucial role both in aging and as a robust physiological antitumor response, which counteracts oncogenic insults. Therefore, via the regulation of senescence, p53 contributes to tumor growth suppression, in a manner strictly dependent by its expression and cellular context. In this review, we focus on the recent advances on the contribution of p53 to cellular senescence and its implication for cancer therapy, and we will discuss p53's impact on animal lifespan. Moreover, we describe p53-mediated regulation of several physiological pathways that could mediate its role in both senescence and aging.
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            This systematic review and meta-analysis were performed to assess the efficacy and tolerability of chemotherapy in patients with advanced gastric cancer. Randomized phase II and III clinical trials on first-line chemotherapy in advanced gastric cancer were identified by electronic searches of Medline, Embase, the Cochrane Controlled Trials Register, and Cancerlit; hand searches of relevant abstract books and reference lists; and contact to experts. Meta-analysis was performed using the fixed-effect model. Overall survival, reported as hazard ratio (HR) with 95% CI, was the primary outcome measure. Analysis of chemotherapy versus best supportive care (HR = 0.39; 95% CI, 0.28 to 0.52) and combination versus single agent, mainly fluorouracil (FU) -based chemotherapy (HR = 0.83; 95% CI = 0.74 to 0.93) showed significant overall survival benefits in favor of chemotherapy and combination chemotherapy, respectively. In addition, comparisons of FU/cisplatin-containing regimens with versus without anthracyclines (HR = 0.77; 95% CI, 0.62 to 0.95) and FU/anthracycline-containing combinations with versus without cisplatin (HR = 0.83; 95% CI, 0.76 to 0.91) both demonstrated a significant survival benefit for the three-drug combination. Comparing irinotecan-containing versus nonirinotecan-containing combinations (mainly FU/cisplatin) resulted in a nonsignificant survival benefit in favor of the irinotecan-containing regimens (HR = 0.88; 95% CI, 0.73 to 1.06), but they have never been compared against a three-drug combination. Best survival results are achieved with three-drug regimens containing FU, an anthracycline, and cisplatin. Among these, regimens including FU as bolus exhibit a higher rate of toxic deaths than regimens using a continuous infusion of FU, such as epirubicin, cisplatin, and continuous-infusion FU.
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              The KEGG database.

              KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.
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                Author and article information

                Journal
                Mol Med Rep
                Mol Med Rep
                Molecular Medicine Reports
                D.A. Spandidos
                1791-2997
                1791-3004
                October 2018
                08 August 2018
                08 August 2018
                : 18
                : 4
                : 3599-3610
                Affiliations
                [1 ]Department of Oncology and Chemotherapy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266031, P.R. China
                [2 ]Department of Tumor Combined Therapy, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266031, P.R. China
                Author notes
                Correspondence to: Professor Wensheng Qiu, Department of Tumor Combined Therapy, The Affiliated Hospital of Qingdao University, 7 Jiaxing Road, Qingdao, Shandong 266031, P.R. China, E-mail: ws_qiu@ 123456126.com
                Article
                mmr-18-04-3599
                10.3892/mmr.2018.9360
                6131613
                30106120
                bc96b314-fdf2-49f2-900e-16c4599794c6
                Copyright: © Qi et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 06 March 2017
                : 26 April 2018
                Categories
                Articles

                gastric cancer,pathway,biomarker,enrichment,different expression

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