Research Papers:

Comparative metabolic and lipidomic profiling of human breast cancer cells with different metastatic potentials

Hye-Youn Kim, Kyung-Min Lee, So-Hyun Kim, Yeo-Jung Kwon, Young-Jin Chun _ and Hyung-Kyoon Choi

PDF  |  HTML  |  Supplementary Files  |  How to cite

Oncotarget. 2016; 7:67111-67128. https://doi.org/10.18632/oncotarget.11560

Metrics: PDF 2895 views  |   HTML 4436 views  |   ?  


Hye-Youn Kim1,*, Kyung-Min Lee1,*, So-Hyun Kim1, Yeo-Jung Kwon1, Young-Jin Chun1, Hyung-Kyoon Choi1

1College of Pharmacy, Chung-Ang University, Seoul 156-756, Republic of Korea

*These authors have contributed equally to this work

Correspondence to:

Young-Jin Chun, email: [email protected]

Hyung-Kyoon Choi, email: [email protected]

Keywords: metastasis, breast cancer cells, metabolomics, lipidomics, PLSR

Received: May 04, 2016     Accepted: August 11, 2016     Published: August 24, 2016


This study conducted comprehensive and comparative metabolic and lipidomic profiling of a human epithelial breast cell line (MCF-10A), a slightly metastatic (MCF-7), and a highly metastatic (MDA-MB-231) breast cancer cell line using gas chromatography mass spectrometry (GC-MS) and direct infusion mass spectrometry (DI-MS). Among 39 metabolites identified by GC-MS analysis, xanthine, glucose-6-phosphate, mannose-6-phosphate, guanine, and adenine were selected as prognostic markers of breast cancer metastasis. Major metabolic pathways involved in differentiation of the cell lines were alanine, aspartate, and glutamate metabolism, purine metabolism and glycine, serine, and threonine metabolism. Among 44 intact lipid species identified by DI-MS analysis, the levels of most phospholipids were higher in both metastatic groups than in normal cells. Specifically, the levels of phosphatidylserine (PS) 18:0/20:4, phosphatidylinositol (PI) 18:0/20:4, and phosphatidylcholine (PC) 18:0/20:4 were markedly higher while those of phosphatidylethanolamine (PE) 18:1/18:1 and PI 18:0/18:1 were lower in MDA-MB-231 cells than in MCF-7 cells. A partial-least-squares regression model was developed and validated for predicting the metastatic potential of breast cancer cells. The information obtained in this study will be useful when developing diagnostic tools and for identifying potential therapeutic targets for metastatic breast cancer.

Creative Commons License All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 11560