Breast cancer detection using targeted plasma metabolomics

Paniz Jasbi, Dongfang Wang, Sunny Lihua Cheng, Qiang Fei, Julia Yue Cui, Li Liu, Yiping Wei, Daniel Raftery, Haiwei Gu

Research output: Contribution to journalArticle

Abstract

Breast cancer (BC) is a major cause of human morbidity and mortality, especially among women. Despite the important role of metabolism in the molecular pathogenesis of cancer, robust metabolic markers to enable enhanced screening and disease monitoring of BC are still critically needed. In this study, a targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolic profiling approach is presented for the identification of metabolic marker candidates that could enable highly sensitive and specific detection of all-stage as well as early-stage BC. In this targeted approach, 105 metabolites from >35 metabolic pathways of potential biological relevance were reliably detected in 201 plasma samples taken from two groups of subjects (102 BC patients and 99 healthy controls). The results of our general linear model and partial least squares-discriminant analysis (PLS-DA) informed the construction of a novel 6-metabolite panel of potential biomarkers. A receiver operating characteristic (ROC) curve generated based on an improved PLS-DA model showed relatively high sensitivity (0.80), specificity (0.75), and area under the receiver-operating characteristic curve (AUROC = 0.89). Similar classification performance of the model was observed for detection of early-stage BC (AUROC = 0.87, sensitivity: 0.86, specificity: 0.75). Bioinformatics analyses revealed significant disturbances in arginine/proline metabolism, tryptophan metabolism, and fatty acid biosynthesis. Our univariate and multivariate results indicate the effectiveness of this metabolomics approach for all-stage as well as early-stage BC diagnosis; our bioinformatics results indicate affected pathways related to tumor growth, metastasis, and immune escape mechanisms. Future studies should validate these results using more samples from different locations.

Original languageEnglish (US)
Pages (from-to)26-37
Number of pages12
JournalJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
Volume1105
DOIs
StatePublished - Jan 15 2019
Externally publishedYes

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Metabolomics
Metabolism
Discriminant analysis
Bioinformatics
Breast Neoplasms
Metabolites
Plasmas
Biosynthesis
Liquid chromatography
Biomarkers
Discriminant Analysis
Computational Biology
Least-Squares Analysis
Proline
ROC Curve
Tryptophan
Mass spectrometry
Arginine
Tumors
Screening

Keywords

  • Biomarker discovery
  • Breast cancer
  • LC-MS/MS
  • Metabolomics
  • Targeted detection

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Clinical Biochemistry
  • Cell Biology

Cite this

Breast cancer detection using targeted plasma metabolomics. / Jasbi, Paniz; Wang, Dongfang; Cheng, Sunny Lihua; Fei, Qiang; Cui, Julia Yue; Liu, Li; Wei, Yiping; Raftery, Daniel; Gu, Haiwei.

In: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, Vol. 1105, 15.01.2019, p. 26-37.

Research output: Contribution to journalArticle

Jasbi, Paniz ; Wang, Dongfang ; Cheng, Sunny Lihua ; Fei, Qiang ; Cui, Julia Yue ; Liu, Li ; Wei, Yiping ; Raftery, Daniel ; Gu, Haiwei. / Breast cancer detection using targeted plasma metabolomics. In: Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences. 2019 ; Vol. 1105. pp. 26-37.
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