Oncotarget

Research Papers:

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

Yong Fan _, Xin Zhou, Tian-Song Xia, Zhuo Chen, Jin Li, Qun Liu, Raphael N. Alolga, Yan Chen, Mao-De Lai, Ping Li, Wei Zhu, Lian-Wen Qi

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Abstract

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

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.

Author Information

Yong Fan
Primary Contact  _

Xin Zhou

Tian-Song Xia

Zhuo Chen

Jin Li

Qun Liu

Raphael N. Alolga

Yan Chen

Mao-De Lai

Ping Li

Wei Zhu

Lian-Wen Qi


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