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Application of ROC curve analysis to metabolomics data sets for the detection of cancer in a mouse model

  • Author / Creator
    Moroz, Jennifer
  • The goal of this study was to show that quantifiable metabolic changes may be used to screen for cancer. NIH III nude mice (n=22) were injected with human GBM cells, with daily urine samples collected pre and post-injection. 14 mice were injected with saline to serve as controls. The measurement of metabolite concentrations took place on an 800 MHz NMR spectrometer. 34 metabolites were identified and quantified, through targeted profiling, with Chenomx Suite 5.1. Univariate statistical analysis showed that 3 metabolites (2-oxoglutarate, glucose and trimethylamine n-oxide) were significantly altered in the presence of tumour, while PCA and PLS-DA models found the maximum variance between the healthy and tumour-bearing groups. Receiver operating characteristic (ROC) curve analysis was applied to the data set to provide a measure of clinical utility. ROC statistics were as high as 0.85 for the analysis of individual metabolites, 0.939 for the analysis of metabolite pairs and 0.996 for PLS-DA models. These results show that metabolomics has potential as a screening tool for cancer.

  • Subjects / Keywords
  • Graduation date
    2010-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3MM2D
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Physics
  • Supervisor / co-supervisor and their department(s)
    • Gino Fallone (Oncology)
    • Alasdair Syme (Oncology)
  • Examining committee members and their departments
    • Keith Wachowicz (Oncology)
    • Jack Tuszynski (Physics)
    • Nicola De Zanche (Oncology)