Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis.
Kidney tumor biomarkers revealed by simultaneous multiple matrix metabolomics analysis.
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DOI：
10.1158/0008-5472.can-11-3105
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发表时间：
2012-07-15
期刊：
影响因子：
11.2
通讯作者：
Weiss RH
中科院分区：
文献类型：
--
作者：
Ganti S;Taylor SL;Abu Aboud O;Yang J;Evans C;Osier MV;Alexander DC;Kim K;Weiss RH
代谢组学越来越多地应用于癌症生物学中，以发现生物标志物和识别潜在的新型治疗靶点。然而，缺乏对多种生物流体进行系统代谢组学研究来确定它们的相互关系并描述它们作为肿瘤替代物的效用。使用肾癌的小鼠异种移植模型，其特征是在被膜下植入 Caki-1 透明细胞人肾癌细胞，我们检查了在基线（尿液）和在或接近动物处死（组织和血浆）时同时获得的组织、血清和尿液。使用 GC 和 LC-MS 完成了所有三个“基质”的统一代谢组学分析。在所有已鉴定的代谢物中（组织中 267 种、血清中 246 种、尿液中 267 种），在所有 3 个基质中均检测到 89 种，并且大多数在相同方向发生改变。各个代谢物的热图显示，血清的变化与组织的关系比尿液的变化更为密切。肉桂酰甘氨酸和烟酰胺这两种代谢物在组织和血清中发生了一致且显着的变化（当针对多重测试进行校正时），而半胱氨酸-谷胱甘肽二硫化物在所有代谢物中显示出最高的变化（组织中的 232.4 倍）。基于这些和其他考虑，选择了三种途径对代谢组数据进行生物学验证，从而确定潜在的治疗靶点。这些数据表明，血清代谢组学分析比尿液更能准确地反映组织变化，色氨酸降解（产生抗炎代谢物）在肾细胞癌中高度表现，并支持 PPAR-α 拮抗可能是这种疾病的潜在治疗方法的概念。
Metabolomics is increasingly being utilized in cancer biology for biomarker discovery and identification of potential novel therapeutic targets. However, a systematic metabolomics study of multiple biofluids to determine their interrelationships and to describe their utility as tumor proxies is lacking. Using a mouse xenograft model of kidney cancer, characterized by sub-capsular implantation of Caki-1 clear cell human kidney cancer cells, we examined tissue, serum, and urine all obtained simultaneously at baseline (urine) and at, or close to, animal sacrifice (tissue and plasma). Uniform metabolomics analysis of all three “matrices” was accomplished using GC- and LC-MS. Of all the metabolites identified (267 in tissue, 246 in serum, 267 in urine), 89 were detected in all 3 matrices, and the majority were altered in the same direction. Heat maps of individual metabolites showed that alterations in serum were more closely related to tissue than was urine. Two metabolites, cinnamoylglycine and nicotinamide, were concordantly and significantly (when corrected for multiple testing) altered in tissue and serum, and cysteine-glutathione disulfide showed the highest change (232.4-fold in tissue) of any metabolite. Based on these and other considerations, three pathways were chosen for biological validation of the metabolomic data, resulting in potential therapeutic target identification. These data show that serum metabolomics analysis is a more accurate proxy for tissue changes than urine, that tryptophan degradation (yielding anti-inflammatory metabolites) is highly represented in renal cell carcinoma, and support the concept that PPAR-alpha antagonism may be a potential therapeutic approach for this disease.