胶质瘤差异表达基因筛选、功能富集和相关信号通路生物信息学分析

Screening function enrichment and related signaling pathways bioinformatics analysis of differentially expressed gene in glioma

目的采用生物信息分析技术探讨胶质瘤(Glioma)差异表达基因筛选、功能富集和相关信号通路。方法在基因表达谱数据库(GEO)中选取胶质瘤中相关基因表达谱芯片数据,采用R软件lima包筛选胶质瘤患者肿瘤组织和正常脑组织中差异表达基因。对筛选出的差异表达基因进行功能富集(GO和KEGG),应用采用蛋白-蛋白相互作用数据库(STRING)分析筛选出的差异表达基因编码蛋白间的相互作用关系,分析相关信号通路。结果选取GSE15824和GSE66354基因表达谱数据集为分析对象,筛选出差异表达超过2倍,且P<0.05的基因158个。158个差异基因主要分子功能(MF)为整合素结合、细胞黏附分子结合、钙离子结合及AMPA谷氨酸受体活性等;细胞组分定位(CC)于细胞膜、神经元细胞体及神经细胞轴突等,而其生物学过程(BP)主要为细胞黏附、神经系统发育、细胞增殖、GTP酶活性、细胞凋亡和血管生成等;KEGG信号通路主要为cAMP信号通路、嘌呤代谢通路、MAPK信号通路及cGMP-PKG信号通路,158个差异表达基因蛋白相互作用网络中相互作用连接共177个,平均每个节点间相互作用为2.39个,聚集系数为0.37。Cytohubb筛选信号通路中的关键基因(hub基因),结果提示,SLC6A1、SLC1A2、BDNF、CAP43、NRXN1、GAD1、OLIG2、PLP1、S100B和GRIA3为相互作用蛋白网络信号通路中的关键基因。10个关键基因均与患者预后有关(P<0.05)。结论胶质瘤患者肿瘤组织和正常组织存在差异表达基因谱,SLC6A1、SLC1A2、BDNF、GAP43、NRXN1、GAD1、OLIG2、PLP1、S100B和GRIA3为胶质瘤发生的关键基因并与患者的预后有关。

Objective To screen the differentially expressed genes, functional enrichment and related signaling pathways in glioma by bioinformatics analysis. Methods Microarray data of glioma related gene expression profiles were selected in GEO database, and differentially expressed genes in glioma patients and normal brain tissues were screened by R statistical software of lima package. Functional enrichment of differentially expressed genes (GO and KEGG) was performed. The protein-protein interaction database (STRING) was used to analyze the interaction between the screened differentially expressed genes and the related signaling pathways. Results Two gene expression profiles, GSE15824 and GSE66354, were selected for analysis, and 158 genes with differential expression more than 2 times and P<0.05 were screened. Molecular function (MF) of 158 differentially expressed genes was integrin binding, cell adhesion molecule binding, calcium binding and AMPA glutamate receptor activity. Cell component localization (CC) was located in cell membrane, neuron cell body, axon of nerve cell and so on, while biological process (BP) was mainly cell adhesion and nervous system. Development, cell proliferation, GTPase activity, apoptosis and angiogenesis;KEGG signaling pathways were mainly cAMP signaling pathway, purine metabolism pathway, MAPK signaling pathway and cGMP-PKG signaling pathway. There were 177 interaction connections in 158 differential expression gene-protein interaction networks, with an average interaction of 2.39 between each node and an aggregation coefficient of 0.37. Cytohubb screened the key genes (hub genes) in the signaling pathway. The results indicated that SLC6A1,SLC1A2,BDNF,GAP43,NRXN1,GAD1,OLIG2, PLP1,S100B and GRIA3 were the key genes in the signaling pathway of the interacting protein network. All the 10 key genes were related to the prognosis of patients (P<0.05). Conclusions There are differentially expressed genes profile in glioma tissues and normal tissues. SLC6A1, SLC1A2, BDNF, GAP43, NRXN1, GAD1, OLIG2, PLP1, S100B and GRIA3 are key genes for glioma development and are related to the prognosis of patients.