上海交通大学学报（医学版）

上海交通大学学报（医学版）

• 论著（基础研究） • 上一篇    下一篇

基于液相色谱-高分辨质谱技术的厄洛替尼获得性耐药肺腺癌细胞代谢轮廓分析

孟爽 1，汪洋 1*，雷绘敏 1，唐亚斌 2，朱亮 2   

  1. 上海交通大学 1. 医学院，2. 基础医学院药理学教研室，上海 200025
  • 出版日期:2017-05-28 发布日期:2017-05-31
  • 通讯作者: 唐亚斌，电子信箱：leonyabin@163.com。
  • 作者简介:孟爽（1992—），女，硕士生；电子信箱：ms3120501@126.com。汪洋（1991—），男，硕士生；电子信箱：18817275718@163.com。*并列第一作者。
  • 基金资助:

    国家自然科学基金（81573018）；上海市青年科技英才——扬帆计划（15YF1406700）

Metabolic profiling analysis associated with acquired erlotinib resistance of lung adenocarcinoma cells based on liquid chromatography–high resolution mass spectrometry

MENG Shuang1, WANG Yang1*, LEI Hui-min1, TANG Ya-bin2, ZHU Liang2   

  1. 1. Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; 2. Department of Pharmacology, Basic Medicine Faculty of Shanghai Jiao Tong University, Shanghai 200025, China
  • Online:2017-05-28 Published:2017-05-31
  • Supported by:

    National Natural Science Foundation of China，81573018；Shanghai Youth Science and Technology Sail Project，15YF1406700

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摘要：

目的 ·探讨表皮生长因子受体酪氨酸激酶抑制剂（EGFR-TKI）厄洛替尼耐药后人肺腺癌细胞（PC9-ER）代谢组学轮廓变化，以发现与耐药相关的差异代谢物组。方法 ·采用超高效液相色谱串联四级杆飞行时间质谱（UPLC-QTOF/MS）技术采集分析PC9-ER细胞及同源亲本PC9细胞的代谢轮廓，并利用偏最小二乘判别分析（PLS-DA）等多维统计手段进一步筛选、鉴定厄洛替尼耐药相关的差异代谢物。结果 ·与PC9细胞相比，PC9-ER细胞共有14种差异代谢物，其中N-乙酰亚精胺、磷脂酰乙醇胺、腺嘌呤核糖核苷酸、泛酸、脯氨酸、谷氨酸、组氨酸等7种代谢物水平上调，瓜氨酸、磷酸胆碱、还原型谷胱甘肽、半胱氨酰甘氨酸、氧化型谷胱甘肽、烟酰胺腺嘌呤二核苷酸、S-腺苷甲硫氨酸等7种代谢物水平下调，主要涉及谷氨酸代谢、谷胱甘肽代谢、氨循环、蛋白质生物合成等代谢通路。结论 ·厄洛替尼耐药后人肺腺癌细胞代谢轮廓发生改变。差异代谢物信息可为耐药新机制和代谢相关药物靶点的发现提供线索。

关键词: 厄洛替尼耐药, 代谢轮廓分析, 超高效液相色谱串联四级杆飞行时间质谱, 肺腺癌

Abstract:

Objective · To explore the change of metabolomic profiling after erlotinib (an epithelial growth factor receptor tyrosine kinase inhibitor) resistance of lung adenocarcinoma cells (PC9-ER), and find the differential metabolome associated with erlotinib resistance. Methods · Metabolic profiling of PC9-ER cells and homologous parent PC9 cells was acquired by the ultraperformance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The data were analyzed by multi-dimensional statistical methods, such as partial least squares projection to latent structures-discriminant analysis (PLS-DA), to select and identify differential metabolites associated with erlotinib resistance. Results · A total of 14 differential metabolites were identified in PC9-ER cells. Seven up-regulated metabolites included N-acetylspermidine, phosphatidylethanolamine, AMP, pantothenic acid, proline, glutamate, and histidine, while seven down-regulated metabolites included citrulline, phosphorylcholine, glutathione, cysteinylglycine, glutathione oxidized, NAD, and S-adenosylmethionine, mainly participating in glutathione metabolism, glutamate metabolism, ammonia recycling, and protein biosynthesis. Conclusion · Metabolic profiling of erlotinib-resistant lung adenocarcinoma cells was changed. The information of differential metabolites associated with erlotinib resistance could provide clues for new resistance mechanisms and potential metabolism-related drug targets.

Key words: erlotinib resistance, metabolic profiling analysis, UPLC-QTOF/MS, lung adenocarcinoma