基于大数据的前列腺癌生物信息学分析

【目的】利用生物信息学的方法，对GEO和TCGA两个基因组学数据库进行分析，探究与前列腺癌相关的差异基因及相关的调控网络。【方法】综合GEO数据库的前列腺癌基因表达芯片数据（GSE46602、GSE55945）和TCGA数据库的RNA-seq数据，利用GEO2R及R语言的edgeR包进行基因差异分析，获得共同的显著差异基因，结合R语言的clusterProfiler包进行GO功能分析及KEGG通路分析，同时利用string网站进行蛋白互作网络分析，筛选出前列腺癌中调节蛋白表达量的关键基因，再结合TCGA临床随访数据分析关键节点基因的临床预后价值。【结果】获得共同差异基因共278个，其中表达上调100个，表达下调178个，它们与上皮细胞的调节增殖、含苯化合物的代谢过程等功能以及谷胱甘肽代谢和粘着斑等信号通路密切相关。蛋白互作网络分析结果得出3个重点蛋白表达模块以及12个关键节点基因。在这些关键基因中，EDN3、EDNRB和AMACR与前列腺癌患者的生存率密切相关。【结论】通过对前列腺癌基因芯片和RNA-seq数据的生物信息学分析，我们发现EDN3、EDNRB与AMACR很可能在前列腺癌的发生发展过程中发挥重要作用。

Bioinformatics Analysis of Prostatic Carcinoma Via Big-Data

【Objective】 The two databases，GEO（gene expression omnibus，GEO）and TCGA（the cancer genome alas ，TCGA），were analyzed using bioinformatics methods to screen differentially expressed genes associated and their related regulatory networks in prostate carcinoma. 【Methods】 The prostate carcinoma gene expression chip data（GSE46602 ，GSE55945） downloaded from the GEO database were integrated into the RNA- seq data of the TCGA database. And the differentially expressed genes analysis was performed using GEO2R and the edgeR package of R software to extract common significant differentially expressed genes. The clusterProfiler package of R software was used to enrich the GO（gene ontology ，GO）function enrichment analysis and KEGG（kyoto encyclopedia of genes and genomes， KEGG）pathway analysis. Differentially expressed genes were further constructed into a protein-protein interaction（PPI） network to screen out key genes for regulatory protein expression in prostate carcinoma. Gene analysis results were combined with TCGA clinical follow-up data to analyze the clinical prognostic value of key node genes. 【Results】A total of 278 significant differentially expressed genes were extracted，of which 178 genes were down- regulated and 100 genes were up-regulated. These genes were closely associated with the function and pathway enrichment such as the regulation of proliferation of epithelial cells，metabolism of benzene- containing compounds，the glutathione metabolism，and focal adhesion. The protein-protein interaction network analysis revealed three key protein expression modules and 12 key node genes. Among these key genes，EDN3（endothelin-3），EDNRB（endothelin receptor B）and AMACR（alpha-methylacyl- coa racemase）were closely related to the survival rate of prostate cancer patients. 【Conclusion】Through bioinformatics analysis of gene chip and RNA-seq data in prostate carcinoma，we found that EDN3，EDNRB and AMACR may play an important role in the occurrence and development of prostate carcinoma.