Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer

// Yong Fan 1, * , Xin Zhou 2, * , Tian-Song Xia 3, * , Zhuo Chen 1 , Jin Li 1 , Qun Liu 1 , Raphael N Alolga 1 , Yan Chen 4 , Mao-De Lai 1 , Ping Li 1 , Wei Zhu 2 , Lian-Wen Qi 1 1 State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China 2 Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China 3 Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China 4 Emergency Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China * These authors have contributed equally to this work Correspondence to: Lian-Wen Qi, e-mail: qilw@cpu.edu.cn Ping Li, e-mail: liping2004@126.com Wei Zhu, e-mail: zhuwei1983213@163.com Keywords: human plasma metabolomics, differential metabolites, molecular subtypes, breast cancer Received: September 04, 2015     Accepted: January 23, 2016     Published: February 03, 2016 ABSTRACT 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.