title: RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer.
abstract: A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.
authors.affiliation: Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, PR China.
authors.forename: Yanhua
authors.initials: Y
authors.lastname: Chen
authors.forename: Ruiping
authors.initials: R
authors.lastname: Zhang
authors.forename: Yongmei
authors.initials: Y
authors.lastname: Song
authors.forename: Jiuming
authors.initials: J
authors.lastname: He
authors.forename: Jianghao
authors.initials: J
authors.lastname: Sun
authors.forename: Jinfa
authors.initials: J
authors.lastname: Bai
authors.forename: Zhuoling
authors.initials: Z
authors.lastname: An
authors.forename: Lijia
authors.initials: L
authors.lastname: Dong
authors.forename: Qimin
authors.initials: Q
authors.lastname: Zhan
authors.forename: Zeper
authors.initials: Z
authors.lastname: Abliz
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism* / Q000652:urine; D001943:Breast Neoplasms / Q000378:metabolism* / Q000652:urine; D016022:Case-Control Studies; D002853:Chromatography, Liquid; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D008875:Middle Aged; D015203:Reproducibility of Results; D053719:Tandem Mass Spectrometry
chemical_list: D014408:Biomarkers, Tumor