Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.
Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.
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DOI：
10.1016/j.juro.2016.01.039
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Putluri N
中科院分区：
文献类型：
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作者：
von Rundstedt FC;Rajapakshe K;Ma J;Arnold JM;Gohlke J;Putluri V;Krishnapuram R;Piyarathna DB;Lotan Y;Gödde D;Roth S;Störkel S;Levitt JM;Michailidis G;Sreekumar A;Lerner SP;Coarfa C;Putluri N
我们使用靶向质谱法研究尿路上皮癌的代谢指纹，并确定生物化学途径分析基因签名是否对膀胱癌患者的独立队列具有预测价值。通过基于液相色谱的靶向质谱法对病理学评价的膀胱衍生组织（包括来自14名患者的良性邻近组织和来自46名患者的膀胱癌）进行分析。通过在15%的错误发现率阈值下调整多重检验的p值，鉴定与肿瘤样品相比与良性组织相关的差异代谢物。使用GO（基因本体）数据库和MSigDB（分子特征数据库）确定与代谢特征相关的途径和过程的富集。将代谢物改变与来自TCGA（癌症基因组图谱）的转录组数据整合，以鉴定30个代谢基因的分子特征。来自TCGA门户网站的可用结果数据用于确定与生存的相关性。我们确定了145种代谢物，其中分析显示31种差异代谢物，比较良性和肿瘤组织样本。使用KEGG（京都基因和基因组百科全书）数据库，我们确定了总共174个基因与改变的代谢途径有关。通过将这些基因与来自相应TCGA数据集的转录组学数据整合，我们鉴定了由30个基因组成的代谢特征。在95例低特征评分患者和282例高特征评分患者中，特征在预测生存方面具有显著性（p = 0.0458）。膀胱癌的靶向质谱对于检测代谢改变是高度敏感的。应用转录组数据允许整合到更大的数据集和识别膀胱癌进展中的相关代谢途径。
We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer. Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival. We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458). Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.