Abstract
PurposeThis work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC).MethodsPlasma 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.ResultsWe 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).ConclusionHuman plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.
Highlights
Breast cancer (BC) is the most common cause of death among women worldwide [1]
Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes
Typical total ion chromatograms (TICs) of a BC sample obtained from ESI+, ESI−, and GC-Q/MS were provided in Supplementary Figure S2
Summary
Breast cancer (BC) is the most common cause of death among women worldwide [1]. Human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER) are the two key molecular biomarkers to segregate the most distinct biologic subgroups of BC [2]. The characteristics of HER2 and ER can be used to roughly divide BC into four major molecular subtypes, including Luminal A (HER2 negative and ER positive), Luminal B (HER2 positive and ER positive), HER2-enriched (HER2 positive and ER negative), and Basal-Like (HER2 negative and ER positive) [3]. Each subtype of BC is accompanied with characteristic molecular features, subsequent metastatic lesions, prognosis and clinical responses to available medical therapies [4]. Repeated biopsies and subsequent histopathology are commonly used to study molecular and genetic information from tumor cells for BC diagnosis and subtype classification. This analysis is invasive and timeconsuming [6, 7]. Rapid and sensitive analysis is urgently required in clinic for discrimination of BC subtypes
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