Department of Clinical Medicine, The First Clinical College, Anhui Medical University, Hefei, Anhui, China (mainland).
Department of Gastrointestinal Surgery Center, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China (mainland).
Med Sci Monit. 2020 Feb 14;26:e920261. doi: 10.12659/MSM.920261.
BACKGROUND Gastric adenocarcinoma accounts for 95% of all gastric malignant tumors. The purpose of this research was to identify differentially expressed genes (DEGs) of gastric adenocarcinoma by use of bioinformatics methods. MATERIAL AND METHODS The gene microarray datasets of GSE103236, GSE79973, and GSE29998 were imported from the GEO database, containing 70 gastric adenocarcinoma samples and 68 matched normal samples. Gene ontology (GO) and KEGG analysis were applied to screened DEGs; Cytoscape software was used for constructing protein-protein interaction (PPI) networks and to perform module analysis of the DEGs. UALCAN was used for prognostic analysis. RESULTS We identified 2909 upregulated DEGs (uDEGs) and 7106 downregulated DEGs (dDEGs) of gastric adenocarcinoma. The GO analysis showed uDEGs were enriched in skeletal system development, cell adhesion, and biological adhesion. KEGG pathway analysis showed uDEGs were enriched in ECM-receptor interaction, focal adhesion, and Cytokine-cytokine receptor interaction. The top 10 hub genes - COL1A1, COL3A1, COL1A2, BGN, COL5A2, THBS2, TIMP1, SPP1, PDGFRB, and COL4A1 - were distinguished from the PPI network. These 10 hub genes were shown to be significantly upregulated in gastric adenocarcinoma tissues in GEPIA. Prognostic analysis of the 10 hub genes via UALCAN showed that the upregulated expression of COL3A1, COL1A2, BGN, and THBS2 significantly reduced the survival time of gastric adenocarcinoma patients. Module analysis revealed that gastric adenocarcinoma was related to 2 pathways: including focal adhesion signaling and ECM-receptor interaction. CONCLUSIONS This research distinguished hub genes and relevant signal pathways, which contributes to our understanding of the molecular mechanisms, and could be used as diagnostic indicators and therapeutic biomarkers for gastric adenocarcinoma.
胃腺癌占所有胃恶性肿瘤的 95%。本研究旨在通过生物信息学方法鉴定胃腺癌的差异表达基因（DEGs）。
从 GEO 数据库中导入 GSE103236、GSE79973 和 GSE29998 的基因微阵列数据集，其中包含 70 例胃腺癌样本和 68 例匹配的正常样本。应用基因本体（GO）和京都基因与基因组百科全书（KEGG）分析筛选 DEGs；使用 Cytoscape 软件构建蛋白质-蛋白质相互作用（PPI）网络，并对 DEGs 进行模块分析。UALCAN 用于预后分析。
我们鉴定出 2909 个上调的 DEGs（uDEGs）和 7106 个下调的 DEGs（dDEGs）的胃腺癌。GO 分析表明，uDEGs 富集于骨骼系统发育、细胞黏附和生物黏附。KEGG 通路分析表明，uDEGs 富集于细胞外基质受体相互作用、粘着斑和细胞因子-细胞因子受体相互作用。从 PPI 网络中区分出前 10 个枢纽基因——COL1A1、COL3A1、COL1A2、BGN、COL5A2、THBS2、TIMP1、SPP1、PDGFRB 和 COL4A1。这些 10 个枢纽基因在 GEPIA 中的胃腺癌组织中显示出明显的上调。通过 UALCAN 对这 10 个枢纽基因的预后分析表明，COL3A1、COL1A2、BGN 和 THBS2 的上调表达显著降低了胃腺癌患者的生存时间。模块分析表明，胃腺癌与 2 条通路有关：包括粘着斑信号通路和细胞外基质受体相互作用。
本研究区分了枢纽基因和相关信号通路，有助于我们理解分子机制，并可作为胃腺癌的诊断指标和治疗生物标志物。
