Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry
Serum metabolic profiling study of lung cancer using ultra high performance liquid chromatography/quadrupole time-of-flight mass spectrometry
复制标题
使用超高效液相色谱/四极杆飞行时间质谱法研究肺癌血清代谢谱
DOI：
10.1016/j.jchromb.2014.04.047
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发表时间：
2014-09-01
影响因子：
3
通讯作者：
Xu, Guowang
中科院分区：
文献类型：
--
作者：
Li, Yanjie;Song, Xue;Xu, Guowang
肺癌目前是全球癌症相关死亡的主要原因。因此，提高对肺癌的认识，增加一种新的肺癌辅助检测工具具有重要意义。本工作采用非靶向代谢组学方法对肺癌患者的血清代谢特征进行了研究。应用超高效液相色谱/四极杆飞行时间质谱仪(UPLC/Q-TOF MS)对23例肺癌患者和23例健康人的代谢谱进行了分析。代谢数据的偏最小二乘判别分析(偏最小二乘判别分析)模型可以清楚地将肺癌患者与健康对照组分开。共鉴定出27种差异代谢物，它们主要与脂代谢紊乱有关，包括胆碱、游离脂肪酸、溶血磷脂酰胆碱等。胆碱和亚油酸被二元Logistic回归定义为一个组合生物标志物，并得到了较小样本集(9例患者和9例健康对照)的验证。这些发现表明，基于LC/MS的血清代谢谱在肺癌患者的补充鉴定中具有潜在的应用前景，可能成为癌症研究的有力工具。(C)2014爱思唯尔B.V.保留所有权利。
Lung cancer is currently the leading cause of cancer-related mortality worldwide. It is, therefore, important to enhance understanding and add a new auxiliary detection tool of lung cancer. In this work, serum metabolic characteristics of lung cancer were investigated with a non-targeted metabolomics method. The metabolic profiling of 23 patients with lung cancer and 23 healthy controls were analyzed using ultra high performance liquid chromatography/quadrupole time of flight mass spectrometry (UPLC/Q-TOF MS). Partial least squares discriminant analysis (PLS-DA) model of the metabolic data allowed the clear separation of the lung cancer patients from the healthy controls. In total, 27 differential metabolites were identified, which were mostly related to the perturbation of lipid metabolism, including choline, free fatty acids, lysophosphatidylcholines, etc. Choline and linoleic acid were defined as one combinational biomarker using binary logistic regression, which was supported by the validation with a smaller sample-set (9 patients and 9 healthy controls). These findings show that LC/MS-based serum metabolic profiling has potential application in complementary identification of lung cancer patients, and could be a powerful tool for cancer research. (C) 2014 Elsevier B.V. All rights reserved.