Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring
Targeted serum metabolite profiling and sequential metabolite ratio analysis for colorectal cancer progression monitoring
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DOI：
10.1007/s00216-015-8984-8
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发表时间：
2015-10-01
影响因子：
4.3
通讯作者：
Raftery, Daniel
中科院分区：
文献类型：
--
作者：
Zhu, Jiangjiang;Djukovic, Danijel;Raftery, Daniel
结直肠癌（CRC）是世界范围内最常见的癌症之一，也是人类发病率和死亡率的主要原因。除了早期发现外，密切监测CRC的疾病进展对于患者预后和治疗决策至关重要。已经努力开发新的方法来改善早期检测和患者监测;然而，使用代谢组学对CRC治疗反应和疾病复发进行监测的研究尚未报道。在这项概念验证研究中，我们应用了一种靶向液相色谱串联质谱（LC-MS/MS）代谢分析方法，重点是对系列血清样本进行连续代谢物比率分析，以监测20例CRC患者的疾病进展。使用连续样本可降低患者间的代谢变异性。使用一组五种代谢物的偏最小二乘判别分析（PLS-DA）模型（琥珀酸，N2，N2-二甲基鸟苷，腺嘌呤，柠康酸，1-甲基鸟苷），并建立了良好的模型性能，（灵敏度= 0.83，特异性= 0.94，获得受试者工作特征曲线（AUROC）下的面积= 0.91，上级传统的CRC监测标志物癌胚抗原（敏感性= 0.75，特异性= 0.76，AUROC = 0.80）。应用蒙特卡罗交叉验证，我们的模型的鲁棒性清楚地观察到从随机排列模型的真实分类模型的分离。我们的研究结果表明代谢谱用于CRC疾病监测的潜在效用。
Colorectal cancer (CRC) is one of the most prevalent cancers worldwide and a major cause of human morbidity and mortality. In addition to early detection, close monitoring of disease progression in CRC can be critical for patient prognosis and treatment decisions. Efforts have been made to develop new methods for improved early detection and patient monitoring; however, research focused on CRC surveillance for treatment response and disease recurrence using metabolomics has yet to be reported. In this proof of concept study, we applied a targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolic profiling approach focused on sequential metabolite ratio analysis of serial serum samples to monitor disease progression from 20 CRC patients. The use of serial samples reduces patient to patient metabolic variability. A partial least squares-discriminant analysis (PLS-DA) model using a panel of five metabolites (succinate, N2, N2-dimethylguanosine, adenine, citraconic acid, and 1-methylguanosine) was established, and excellent model performance (sensitivity = 0.83, specificity = 0.94, area under the receiver operator characteristic curve (AUROC) = 0.91 was obtained, which is superior to the traditional CRC monitoring marker carcinoembryonic antigen (sensitivity = 0.75, specificity = 0.76, AUROC = 0.80). Monte Carlo cross validation was applied, and the robustness of our model was clearly observed by the separation of true classification models from the random permutation models. Our results suggest the potential utility of metabolic profiling for CRC disease monitoring.