Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer
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Yong Fan1,*, Xin Zhou2,*, Tian-Song Xia3,*, Zhuo Chen1, Jin Li1, Qun Liu1, Raphael N Alolga1, Yan Chen4, Mao-De Lai1, Ping Li1, Wei Zhu2, Lian-Wen Qi1
1State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
2Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
3Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
4Emergency Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
*These authors have contributed equally to this work
Lian-Wen Qi, e-mail: email@example.com
Ping Li, e-mail: firstname.lastname@example.org
Wei Zhu, e-mail: email@example.com
Keywords: human plasma metabolomics, differential metabolites, molecular subtypes, breast cancer
Received: September 04, 2015 Accepted: January 23, 2016 Published: February 03, 2016
Purpose: This work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).
Methods: Plasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis.
Results: We observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45).
Conclusion: Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.
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