A Comprehensive Workflow of Mass Spectrometry-Based Untargeted Metabolomics in Cancer Metabolic Biomarker Discovery Using Human Plasma and Urine

Metabolites

Wei Zou¹  Jianwen She¹ 

[1]California Department of Public Health, 850 Marina Bay Parkway, G365, Richmond, CA 94804, USA

[2] E-Mail:

关键词: untargeted metabolomics;    pathway analysis;    HILIC;    RP;    TOF;    FT-ICR;    Orbitrap;    QTrap;    mass spectrometer;    cancer;    human;    plasma;    urine;   

DOI  :  10.3390/metabo3030787

来源: mdpi

【 摘 要 】

Current available biomarkers lack sensitivity and/or specificity for early detection of cancer. To address this challenge, a robust and complete workflow for metabolic profiling and data mining is described in details. Three independent and complementary analytical techniques for metabolic profiling are applied: hydrophilic interaction liquid chromatography (HILIC–LC), reversed-phase liquid chromatography (RP–LC), and gas chromatography (GC). All three techniques are coupled to a mass spectrometer (MS) in the full scan acquisition mode, and both unsupervised and supervised methods are used for data mining. The univariate and multivariate feature selection are used to determine subsets of potentially discriminative predictors. These predictors are further identified by obtaining accurate masses and isotopic ratios using selected ion monitoring (SIM) and data-dependent MS/MS and/or accurate mass MSⁿ ion tree scans utilizing high resolution MS. A list combining all of the identified potential biomarkers generated from different platforms and algorithms is used for pathway analysis. Such a workflow combining comprehensive metabolic profiling and advanced data mining techniques may provide a powerful approach for metabolic pathway analysis and biomarker discovery in cancer research. Two case studies with previous published data are adapted and included in the context to elucidate the application of the workflow.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.