LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery
LC-MS based serum metabonomic analysis for renal cell carcinoma diagnosis, staging, and biomarker discovery
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DOI：
10.1021/pr101161u
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发表时间：
2011-03-01
影响因子：
4.4
通讯作者：
Hang, Wei
中科院分区：
文献类型：
--
作者：
Lin, Lin;Huang, Zhenzhen;Hang, Wei
采用反相(RP)层析和亲水作用层析(HILIC)相结合的LC-MS方法，结合多变量数据分析，对肾细胞癌(RCC)患者和健康对照组的血清总谱进行了区分。HILIC被发现对于全面的血清代谢组学分析以及RP分离是必要的。评价了血清代谢组学用于肾癌诊断和分期的可行性。检测灵敏度达到100%，观察到早期和晚期患者之间有令人满意的聚集性。结果表明，LC-MS分析与多元统计分析相结合可用于肾癌的诊断，对肾癌的分期具有潜在的应用价值。进行了MS/MS实验，以确定对鉴别做出重大贡献的生物标志物模式。结果，确定了30个潜在的肾癌生物标志物。目前的生物标记物模式可能不是肾癌独有的，而只是任何恶性肿瘤的结果。为了进一步阐明肾细胞癌的病理生理机制，相关代谢途径已被研究。肾癌与磷脂代谢紊乱、鞘脂代谢、苯丙氨酸代谢、色氨酸代谢、脂肪酸β氧化、胆固醇代谢和花生四烯酸代谢密切相关。
A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.