胃癌关键基因和通路的生物信息学和功能分析  

Bioinformatics and Functional Analysis of Key Genes and Pathways in Gastric Cancer

作　　者：吴茜 宋兴勃[1] 钟慧钰 温阳 应斌武[1] Wu Qian;Song Xingbo;Zhong Huiyu;Wen Yang;Ying Binwu(Department of Laboratory Medicine,West China Hospital,Sichuan University,Chengdu 610041,Sichuan,China)

机构地区：[1]四川大学华西医院实验医学科

出　　处：《肿瘤预防与治疗》2020年第2期131-139,共9页Journal of Cancer Control And Treatment

基　　金：国家自然科学基金(编号:81672096)~~

摘　　要：目的:本研究拟通过生物信息学分析的方法分析胃癌及癌旁正常组织差异表达的基因,探索胃癌的发病机制,为胃癌的早期诊断和治疗评估提供新思路。方法:从基因表达数据库中获取GSE79973和GSE19826基因表达谱,使用GCBI在线实验室筛选差异表达基因,并使用基因本体论分析、代谢通路分析、基因信号通路网络分析、共表达分析对所获得基因进行分析。结果:与对照组相比,在胃癌组织中筛选出共1 206个基因呈差异表达。其中,542个基因表达下调,664个基因表达上调;差异基因主要涉及细胞粘附、细胞外基质组织、细胞外基质分解、胶原蛋白分解代谢过程等方面,pathway分析发现核心信号通路主要涉及粘附力、糖酵解/糖异生、Wnt信号通路、癌症通路等方面;基因信号通路网络分析发现的关键节点基因为UGT2B15、ITGA2、ITGB1、CYP3A4;共表达网络分析推测的关键节点基因为SH3GL2、CKMT2、CHIA、ATP4A。结论:INHBA、UGT2B15、ITGA2、ITGB1、SH3GL2等基因及其相关的生物过程可能是胃癌的潜在生物标志物和治疗靶标,生物信息学有助于全面深入研究疾病发生机制,筛选可能的核心靶点,为临床诊断及疾病治疗提供参考。Objective: The study aims to explore the pathogenesis of gastric cancer(GC) and provide new biomarkers for early diagnosis and treatment evaluation of GC. Methods: GSE79973 and GSE19826 gene expression profiles were obtained from the gene expression database. Differentially expressed genes(DEGs) were screened by using Gene-Cloud of Biotechnology Information platform. Gene ontology enrichment analysis, pathway analysis, pathway and network analysis and co-expression analysis were performed to analyze DEGs. Results: Compared with the control group, 1206 genes were differentially expressed in GC tissue. Among them, 542 genes were down-regulated, and 664 genes were up-regulated;DEGs mainly involved cell adhesion, extracellular matrix tissue, extracellular matrix disassembly, collagen catabolism. Pathway analysis revealed that the core signaling pathway mainly involved adhesion, gluconeogenesis, Wnt signaling pathway, cancer pathway and so on. Pathway and network analysis found that key hub genes were UGT2 B15, ITGA2, ITGB1 and CYP3 A4. Co-expression network analysis speculated that key hub genes were SH3 GL2, CKMT2, CHIA and ATP4 A. Conclusion: Genes such as GKN2, GKN1, ATP4 B, UGT2 B15, SH3 GL2 and related biological processes may be potential biomarkers and therapeutic targets for GC. Bioinformatics can help us to comprehensively study the mechanism of disease occurrence and identify possible core targets, which can provide potential targets for the diagnosis and treatment of the disease.

关 键 词：生物信息学 胃癌 GEO数据库 GCBI 

分 类 号：R735.2[医药卫生—肿瘤] R319[医药卫生—临床医学]