胃癌相关核心基因的生物信息学分析

doi:10.3969/j.issn.1004-616x.2019.04.008

摘要/Abstract

摘要： 目的：筛选胃癌相关的核心基因及其与胃癌诊断、预后的关系，为胃癌分子诊断、靶向治疗、预后评判提供研究方向。方法：从基因表达数据库（GEO）下载胃癌相关的mRNA表达谱芯片数据，利用R软件的Limma包分别筛选出胃癌组织中较癌旁组织相比具有显著差异表达的基因（DEGs），在基因功能注释数据库（DAVID）中对DEGs进行基因本体（GO）功能注释及京都基因与基因组百科全书（KEGG）富集分析，交互基因检索工具（STRING）及Cytoscape软件的网络分析插件CytoHubba用于构建蛋白互作网络（PPI）并进行可视化分析，筛选出核心基因；利用生存分析工具（KM数据库）分析核心基因与胃癌患者预后的关系，并量化具有预后意义的核心基因的诊断价值，利用GraphPad软件将其可视化。最后用皮尔逊（Pearson）法检验核心基因之间的相关性。结果：在GEO数据库得到的3个基因芯片表达谱中，胃癌组织与正常组织差异表达显著的基因有1 839个，上调基因851个，下调基因988个，三者取交集后，在3个表达谱芯片中均有显著差异表达的基因有66个，上调基因24个，下调基因42个；GO富集分析显示，差异表达基因的功能主要集中在细胞外空间、细胞外外泌体、消化、细胞外基质组织、胶原纤维组织；KEGG富集分析提示，差异表达基因的通路主要涉及蛋白质消化和吸收、胃酸分泌、氮代谢、ECM-受体相互作用、矿物质吸收等；PPI网络中，Cytoscape可视化分析发现10个核心基因的差异表达与胃癌的发生密切相关，KM数据库检索发现，FN1、COL1A1低表达组患者预后更佳，高表达组更差。FN1、COL1A1的AUC分别为0.93、0.90，提示两者均具有较高的诊断价值，两者的相关性分析得出相关系数r=0.59（P < 0.05），提示COL1A1与FN1两者在胃癌中的表达呈正相关。结论：生物信息分析筛选的核心基因FN1、COL1A1可能成为提示胃癌患者预后、早期诊断、研发胃癌靶向药物的候选标志物或靶点。

关键词: 胃癌, 生物信息学, 差异基因, 靶点, 诊断, 预后

Abstract: OBJECTIVE:Apply bioinformatics analyses to explore relationships between hub genes and diagnosis/prognosis of gastric cancer and to possibly enhance molecular diagnosis,targeted therapy and prognosis of gastric cancer. METHODS:Three gastric cancer-associated mRNA expression profiles were down-loaded from the GEO database. The Limma package of R software was used to screen out differentially expressed genes (DEGs) in microarrays. Then,Funrich software was employed to take the intersection of the three expression profiling chips,and further obtain DEGs in all three expression profiles. DAVID (Database for Annotation,Visualization and Integrated Discovery) database was utilized to do the GO function annotation and KEGG enrichment analysis for DEGs,STRING online website and Cytoscape software network analysis,plug-in CytoHubba were applied to construct protein interaction network (PPI) and visual analysis,and then screen out the core genes (Hub Gene). The relationship between core genes and prognosis of patients with gastric cancer was analyzed in KM database. The diagnostic value of core genes with prognostic significance was quantified and visualized by GraphPad software. Finally,Pearson method was used to examine correlations among core genes. RESULTS:In the three datasets,there were 1 839 DEGs,851 up-regulated genes,and 988 down-regulated genes. After screening out overlapping genes,there were 66 genes with significant differential expression in the three expression profiles,consisting of 24 up-regulated genes and 42 down-regulated genes. GO enrichment analyses show that the functions of DEGs were mainly concentrated in extracellular space,extracellular exosomes,digestion,extracellular matrix tissue,and collagen fibrous tissue. KEGG enrichment analyses show that involved pathways were mainly in protein digestion and absorption,gastric acid secretion,nitrogen metabolism,ECM-receptor interaction,mineral absorption. In the PPI network,Cytoscape visual analyses show that differential expression of the 10 core genes was closely related to the occurrence of gastric cancer. KM database searches identify that patients with low expression of FN1 and COL1A1 had better prognosis than those with higher expression. The AUC of FN1 and COL1A1 were 0.93 and 0.90,respectively,indicating that both had high diagnostic values. The correlation coefficient,R=0.59,was obtained through the Pearson Correlation analyses which suggest that the expression of COL1A1 and FN1 in gastric cancer was positively correlated. CONCLUSION:The data show that both FN1 and COL1A1 can potentially be important targets for prognosis,early diagnosis and development of targeted drugs for gastric cancer.

Key words: gastric cancer, bioinformatics, differentially expressed genes, prognosis, treatment, target

中图分类号:

R730.2

陈秀琼, 孟凡桥, 熊华, 王雅丽, 周洋媚, 唐雯华, 邹燕梅. 胃癌相关核心基因的生物信息学分析[J]. 癌变·畸变·突变, 2019, 31(4): 308-314.

CHEN Xiuqiong, MENG Fanqiao, XIONG Hua, WANG Yali, ZHOU Yangmei, TANG Wenhua, ZOU Yanmei. Investigation and evaluation of gastric cancer core genes using bioinformatics[J]. Carcinogenesis, Teratogenesis & Mutagenesis, 2019, 31(4): 308-314.

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