• Original article (Basic research) • Previous Articles     Next Articles

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


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