文献类型：期刊文献

中文题名：突变型与野生型胃肠道间质瘤基因筛选及信号通路分析

英文题名：Screening of Differential Genes between Mutant and Wild Type Gastrointestinal Stromal Tumors and Analysis of Related Functions and Signal Pathways

作者：何毅刚[1];石鑫[2];于建平[2];王婧[3];张亚男[4];刘宏斌[2];陈为凯[4]

第一作者：何毅刚

机构：[1]中国人民解放军联勤保障部队第940医院,兰州730000;[2]泸州市人民医院普通外科,646000;[3]甘肃省干细胞与基因药物重点实验室,兰州730000;[4]甘肃中医药大学临床医学院,兰州730000

第一机构：中国人民解放军联勤保障部队第940医院,兰州730000

年份：2020

卷号：49

期号：4

起止页码：39

中文期刊名：医学研究杂志

外文期刊名：Journal of Medical Research

收录：CSTPCD

基金：国家科技部、财政部科技惠民计划项目(2012GS620101);甘肃省自然科学基金资助项目(1506RJZA309);甘肃中医药大学研究生创新基金资助项目(2019CX35、2019CX29)。

语种：中文

中文关键词：胃肠道间质瘤;差异基因;基因本体;信号通路;生物信息学

外文关键词：Gastrointestinal stromal tumors;Differential genes;Gene noumenon;Signaling pathway;Bioinformatics

摘要：目的筛选参与调控KIT/PDGFRA野生型胃肠道间质瘤(WT-GIST)发生、发展的关键基因及其信号通路,探索WT-GIST潜在的分子标志物。方法下载来自GEO数据库GSE17743、GSE20708、GSE1325423个GIST基因芯片数据集。合并3个芯片的基因表达矩阵、对其进行标准化处理并去除合并矩阵的批次效应、应用主成分分析检测其标准化情况后,将3个芯片中WT-GIST样本与KIT/PDGFRA突变型GIST(MUT-GIST)样本进行联合生物信息学分析,确定两组间差异表达基因(DEGs),并利用DAVID和KOBAS数据库对DEGs分别进行生物功能(gene ontology,GO)及信号通路(kyoto encyclopedia of genes and genomes,KEGG)富集分析。利用STRING数据库及CYTOSCAPE软件进行DEGs蛋白间相互作用网络(protein protein interaction network,PPI)的绘制及可视化处理,利用cytoHubba、MCODE插件划分子网络并筛选关键基因(HUBs),最后应用DAVID及KOBAS数据库对HUBs进行功能富集分析。结果本研究共筛选出628个DEGs,其中包括226个上调基因,402个下调基因。DEGs的GO分析结果提示其主要富集于“蛋白质的结合”、“跨膜转运”、“细胞膜的构成”、“外泌体的形成”等功能。KEGG分析显示DEGs主要富集于“代谢途径”、“PI 3K-Akt信号通路”、“神经性配体-受体相互作用”等信号通路。通过构建PPI网络筛选出了PENK、GRIA2、FBXO6、KLHL13、HERC5等25个HUBs,GO分析结果显示其主要富集于“细胞外谷氨酸门控离子通道活性”等功能,KEGG分析结果显示其主要富集于“神经性配体-受体相互作用”、“逆行内源性大麻素信号转导通路”等信号通路。结论通过合并3个基因芯片的基因表达矩阵,利用生物信息学方法筛选出参与WT-GIST发生、发展过程的关键基因和信号通路,为WT-GIST的诊疗探索潜在靶点。
Objective To screen the key genes and signal pathways involved in the regulation of the occurrence and development of KIT/PDGFRA wild type gastrointestinal stromal tumor(WT-GIST)and to explore the potential molecular markers of WT-GIST.Methods Three GIST microarray datasets from GEO database GSE17743,GSE20708 and GSE132542 were downloaded.After merging the gene expression matrix of the three chips,standardizing it and removing the batch effect of the merging matrix,and using principal component analysis to detect its standardization.The WT-GIST samples in the three chips and the KIT/PDGFRA mutant GIST(MUT-GIST)samples were combined with bioinformatics analysis to determine the differentially expressed gene(DEGs),between the two groups,and the DEGs was used for biofunctional(gene ontology,using DAVID and KOBAS databases,respectively.GO)and signal pathway(Kyoto encyclopedia of genes and genomes,KEGG)enrichment analysis.The DEGs protein interaction network(protein interaction network,PPI)was plotted and visually processed by STRING database and CYTOSCAPE software.The molecular network was divided into molecular networks using cytoHubba and MCODE plugins and the key gene(HUBs),was screened.Finally,the function enrichment analysis of HUBs is carried out by using DAVID and KOBAS database.Results In this study,a total of 628 DEGs,were screened,including 226 up-regulated genes and 402 down-regulated genes.GO analysis of DEGs was mainly concentrated in“protein binding”,“transmembrane transport”,“membrane composition”,“exocrine formation”and so on.KEGG analysis showed that DEGs was mainly enriched in“metabolic pathway”,“PI 3K-Akt signal pathway”,“neurogenic ligand-receptor interaction”and so on.Through the construction of PPI,25 Hub genes such as PENK,GRIA2,FBXO6,KLHL13 and HERC5 were screened,and their GO analysis was mainly enriched in“extracellular glutamate gated ion channel activity”.KEGG analysis was mainly concentrated in“neural ligand-receptor interaction”,“retrograde endogenous marijuana signal transduction pathway”and so on.Conclusion By combining the gene expression matrix of three gene chips,the key genes and signal pathways in the occurrence and development of WT-GIST were screened by bioinformatics method,so as to explore the potential target for the diagnosis and treatment of WT-GIST.