膀胱癌关键基因及通路的生物信息学分析

Bioinformatics analysis of hub genes and signal pathway in bladder cancer

目的:运用生物信息学技术对膀胱癌基因表达谱分析获取差异基因及其相关信号通路。方法:利用生物信息学技术及相关工具对GSE13507、GSE7476、GSE40355、癌症基因图谱计划(TCGA)数据库中膀胱癌基因表达序列数据集行差异表达分析并获取共同差异基因379个,使用STRING数据库、Cytoscape软件获取蛋白互作网络(PPI)及关键基因,clusterProfiler包对10个关键基因行基因本体论(GO)、生物信号通路(KEGG)富集分析,最后利用人类蛋白表达图集(HPA)在线工具对10个关键基因行生存分析。结果:差异分析获取上调基因77个、下调基因302个,共379个。MCODE提取得到2个主要的PPI网络,cytoHubba共筛选出10个关键基因[细胞周期蛋白依赖性激酶1(CDK1)、细胞周期蛋白B1(CCNB1)、泛素结合酶E2 C(UBE2C)、细胞分裂周期蛋白20(CDC20)、胸苷酸合成酶(TYMS)、极光激酶A(AURKA)、哺乳动物叉头框蛋白M1(FOXM1)、细胞周期蛋白B2(CCNB2)、PDZ结合激酶(PBK)、细胞分裂蛋白调节剂1(PRC1)],均为上调基因。通过GO、KEGG富集分析,关键基因主要富集在蛋白酶代谢、细胞周期与细胞分裂等功能中,以及细胞周期、细胞衰老、p53信号通路、FoxO信号通路等信号通路上。生存分析结果显示CCNB1、CDC20、PRC1在癌组织中高表达,与较差的预后相关(P<0.05),差异有统计学意义。结论:通过生物信息学分析与挖掘有助于认识膀胱癌的发生发展,CCNB1、CDC20、PRC1有助于膀胱癌诊断及治疗。

Objective Differential genes and related signal pathways in bladder cancer were analyzed by using bioinformatics technology.Methods Bioinformatics technology and related tools were used to analyze the differential expressed genes and 379 common differential genes of bladder cancer were obtained in the GSE13507,GSE7476,GSE40355,and the cancer genome atlas(TCGA)databases.STRING database and Cytoscape software were used to obtain protein interaction network(PPI)and hub genes.Then,cluster Profiler package was used to perform gene ontology(GO)and kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis of 10 hub genes.Finally,the human protein atlas(HPA)was used to perform survival analysis of 10 hub genes.Results The difference analysis obtained 77 up-regulated genes and 302 down-regulated genes,a total of 379 genes.MCODE extracted two main PPI networks,and cytoHubba screened a total of 10 key genes[cyclin dependent kinase 1(CDK1),Cyclin B1(CCNB1),Ubiquitin conjugating enzyme E2 C(UBE2C),cell division cycle 20(CDC20),thymidylate synthetase(TYMS),aurora kinase A(AURKA),forkhead box M1(FOXM1),Cyclin B2(CCNB2),PDZ binding kinase(PBK),protein regulator of cytokinesis 1(PRC1)],all of which were up-regulated genes.Through GO and KEGG enrichment analysis,key genes were mainly enriched in functions such as protease metabolism,cell cycle and cell division,as well as signal pathways such as cell cycle,cell senescence,p53 signaling pathway,and FoxO signaling pathway.Survival analysis showed that CCNB1,CDC20 and PRC1 were highly expressed in cancer tissues,which were related to poor prognosis(P<0.05).Conclusion Bioinformatics analysis and mining can help people further understand the occurrence and development of bladder cancer.The results of this study suggest that CCNB1,CDC20 and PRC1 may be potential targets for diagnosis and treatment of bladder cancer.