【作者】 苑锋； 明浩朗； 任炳成； 于圣平； 杨亦寒； 李涛； 易立； 杨学军；

【Author】 Yuan Feng;Ming Haolang;Ren Bingcheng;Yu Shengping;Li Tao;Yi Ling;Yang;Xuejun Department of Neurosurgery,General Hospital of Tianjin Medical University;

【机构】 天津医科大学总医院神经外科；

【摘要】 目的:利用癌症基因组图谱(TCGA)计划数据库胶质瘤标本信息,分析在PDK1在胶质母细胞瘤(GBM)中的分子及临床特点。方法:在TCGA数据库中提取1208例胶质瘤标本中基因表达量数据及患者的临床数据,R软件被用来作为统计分析和图形工作的主要工具,用STRING构建关键基因蛋白相互作用网络,用DAVID对关键基因进行功能富集分析,再用GEPIA对关键基因进行生存分析。结果:相对于正常对照,PDK1在胶质瘤中高表达,且级别越高表达越高,并发现高表达PDK1的患者预后较差;在GBM患者中发现与PDK1表达密切相关(Pearson|R|> 0.4)的基因共182个;蛋白互相作用关系中可信度最大的前7位基因,得到前10个功能富集数据结果提示:PDK1在GBM中糖酵解代谢途径、核苷酸代谢途径、ATP合成及缺氧环境中发挥重要作用;生存分析结果显示7个基因与患者的生存预后关系。结论:基于癌症和肿瘤基因图谱挖掘数据是一种有效的寻找治疗靶点的生物信息手段;PDK1表达与胶质瘤患者预后有关,并且与糖酵解代谢途径、核苷酸代谢途径、ATP合成及缺氧微环境密切相关,提示其可作为潜在的预后及治疗的新靶点。更多还原

【Abstract】 Objective We aimed at investigating the role of PDK1 at transcriptome level and its relationship with clinical practice in glioma from The Cancer Genome Atlas(TCGA) database. Methods A cohort of 1208 glioma patients with RNA-seq data from TCGA was analyzed, R language was used as the main tool for statistical analysis and graphical work. We performed gene function enrichment analysis via DAVID Bioinformatics Resources, constructed protein interaction networks using STRING software, and used GEPIA to analyze survival data base on the median total patient sample combined with clinical data. Results Contrasting to normal tissue or lower grade glioma, PDK1 was signifcantly upregulated in GBM patients according to TCGA dataset(Figure 1 a,c,d). Accordingly, The Kaplan-Meier survival analysis revealed that patients with high levels of PDK1 possessed shorter overall survival(OS) time than patients with low levels of PDK1, P = 0(Figure 1 b). To clarify the biologic role of PDK1 in GBM, we performed GO analysis. First, we created a gene list that strongly correlated with PDK1 by Pearson correlation analysis(Pearson |R|> 0.4). Finally, there were 182 genes in TCGA gene list. Then, Among the top 7 genes with the combined score in the protein interaction relationship, Finally, we explored the biofunction of these genes respectively by GO analysis in DAVID Bioinformatics Resources 6.8. When the gene function was sorted by p value in increasing order, genes most relevant to PDK1 were mostly involved in glycolytic process, nucleoside diphosphate metabolic process, ADP metabolic process, cellular response to hypoxia in TCGA database(Figure 2 and 3). The survival analysis results show 6 of 7 key genes were associated with improved survival outcomes(P<0.05)(Figure 4). Conclusion Data mining from the TCGA is an effective means of exploring biological information of glioma therapeutic targets. PDK1 expression is associated with prognosis in patients with glioma, and is closely related to glycolytic process, nucleotide metabolism pathway, ATP synthesis and hypoxic microenvironment, suggesting that it can be used as a new target for potential prognosis and treatment.更多还原