摘要
目的分析糖尿病患者与正常人肝组织之间的差异表达基因(DEGs),筛选出肝组织中与可能产生胰岛素抵抗相关作用的蛋白分子。方法从GEO数据库下载基因芯片数据集GSE23343(包括10例2型糖尿病和7例正常人肝组织的基因表达值),通过DEGs表达谱分析和功能通路富集分析,构建DEGs对应的蛋白质-蛋白质相互作用网络。结果分析得到928个显著上调的DEGs(P<0.01),发现DEGs主要富集在细胞和代谢生物过程中,KEGG通路富集显示DEGs主要集中于信号转导和肿瘤相关通路。经蛋白质相互作用网络构建,筛选出5个关键蛋白分子MDM2、PCNA、CAV1、PIK3R1、NR3C1。结论系统地筛选出人类肝组织中可能与胰岛素抵抗形成相关的蛋白分子,为进一步实验研究肝胰岛素抵抗产生机制和新的降血糖药物作用靶点提供基础。
Objective To analyze of differentially expressed genes(DEGs)between diabetic and normal liver tissues and to screen proteins in liver tissues that may be associated with insulin resistance.Methods After download the gene expression profile GSE23343(including gene expression value from 10 cases of type 2 diabetes mellitus and 7 cases of normal liver)from the GEO Database,and the protein-protein interaction network of DEGs was constructed by DEGs expression profiling and functional pathway enrichment analysis.Results After analysis of the microarray data,928 DEGs were found to be significantly up-regulated(P<0.01).Functional pathway enrichment analysis showed that most of the differential genes were enriched in cellular and metabolic processes,and KEGG pathway enrichment showed that DEGs were mainly focused on signal transduction and tumor-associated pathways.Five key protein molecules,e.g.MDM2,PCNA,CAV1,PIK3R1,and NR3C1,were identified by protein-protein interaction network.Conclusion The protein molecules related to the formation of insulin resistance in human liver tissues are systematically selected,which might provide us the basis for the further study of the mechanism of hepatic insulin resistance and for finding new targets of hypoglycemic medicine.
作者
黄发光
李小成
龚建平
李泽民
Huang Faguang;Li Xiaocheng;Gong Jianping(Department of Surgery,People's Hospital,Wuxi County 405800,Chongqing,China;Department of Surgery,Second Hospital,Chongqing Medical University,Chongqing)
出处
《实用肝脏病杂志》
CAS
2018年第6期872-876,共5页
Journal of Practical Hepatology
基金
重庆市卫生与计划生育委员会科研项目面上项目(编号:2016MSXM206)
关键词
非酒精性脂肪性肝病
胰岛素抵抗
差异基因
蛋白质-蛋白质相互作用网路
Nonalcoholic fatty liver diseases
Insulin resistance
Differentially expressed genes
Protein-protein interaction network
