Shuanhu Wang, Song Tao, Yakui Liu, Yi Shi, Mulin Liu
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引用次数: 0
Abstract
Background: Gastric cancer (GC) ranks second in mortality among all malignant diseases worldwide. However, the cause and molecular mechanism underlying gastric cancer are not clear. Here, we used integrated bioinformatics to identify possible key genes and reveal the pathogenesis and prognosis of gastric cancer.
Methods: The gene expression profiles of GSE118916, GSE79973, and GSE29272 were available from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between GC and normal gastric tissues were screened by R software and Venn diagram software. GO and KEGG pathway enrichment of DEGs was performed using the DAVID database. A protein-protein interaction (PPI) network was established by STRING and visualized using Cytoscape software. Then the influence of hub genes on expression and survival was assessed using TCGA database.
Results: A total of 83 DEGs were found in the three datasets, including 41 up-regulated genes and 42 down-regulated genes. These DEGs were mainly enriched in extracellular matrix organization and cell adhesion. The enriched pathways obtained in the KEGG pathway analysis were extracellular matrix (ECM)-receptor interaction and focal adhesion. A PPI network of DEGs was analyzed using the Molecular Complex Detection (MCODE) app of Cytoscape. Four genes were considered hub genes, including COL5A1, FBN1, SPARC, and LUM. Among them, LUM was found to have a significantly worse prognosis based on TCGA database.
Conclusions: We screened DEGs associated with GC by integrated bioinformatics analysis and found one potential biomarker that may be involved in the progress of GC. This hub gene may serve as a guide for further molecular biological experiments.
背景:胃癌在全球恶性疾病中死亡率居第二位。然而，胃癌的病因和分子机制尚不清楚。在此，我们利用综合生物信息学方法鉴定可能的关键基因，揭示胃癌的发病机制和预后。方法:从gene expression Omnibus (GEO)数据库中获取GSE118916、GSE79973和GSE29272的基因表达谱。采用R软件和维恩图软件筛选胃癌组织与正常胃组织的差异表达基因(DEGs)。使用DAVID数据库对DEGs进行GO和KEGG途径富集。利用STRING建立蛋白相互作用(PPI)网络，并利用Cytoscape软件进行可视化。然后使用TCGA数据库评估枢纽基因对表达和生存的影响。结果:三个数据集中共发现83个deg，其中上调基因41个，下调基因42个。这些deg主要富集于细胞外基质组织和细胞粘附。KEGG途径分析中得到的富集途径是细胞外基质(ECM)-受体相互作用和局灶黏附。使用Cytoscape的分子复合物检测(MCODE)应用程序分析了DEGs的PPI网络。四个基因被认为是枢纽基因，包括COL5A1、FBN1、SPARC和LUM。其中，根据TCGA数据库发现LUM的预后明显较差。结论:我们通过综合生物信息学分析筛选了与GC相关的deg，并发现了一个可能参与GC进展的潜在生物标志物。该枢纽基因可作为进一步分子生物学实验的指导。
期刊介绍：
As the official publication of the National Cancer Institute, Cairo University, the Journal of the Egyptian National Cancer Institute (JENCI) is an open access peer-reviewed journal that publishes on the latest innovations in oncology and thereby, providing academics and clinicians a leading research platform. JENCI welcomes submissions pertaining to all fields of basic, applied and clinical cancer research. Main topics of interest include: local and systemic anticancer therapy (with specific interest on applied cancer research from developing countries); experimental oncology; early cancer detection; randomized trials (including negatives ones); and key emerging fields of personalized medicine, such as molecular pathology, bioinformatics, and biotechnologies.
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