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Author Notes:

Corresponding Author: zhen.qi@gatech.edu, Tel: 404-385-4761.

Z.Q. and E.O.V. designed research.

Z.Q. performed research and analyzed results.

Z.Q. and E.O.V. wrote the paper.

All authors critically reviewed content and approved the final version for publication.

The authors have no conflict of interest to declare.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsoring institutions.

Subjects:

Research Funding:

This work was supported by a grant from the National Institutes of Health (P01-ES016731, GWM, PI), an endowment from the Georgia Research Alliance (EOV, PI), and a Pilot and Feasibility award (ZQ, PI) from the NIH Regional Comprehensive Metabolomics Resource Core grant 1U24DK097215-01A1 (RMH, PI).

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biochemistry & Molecular Biology
  • PURINE METABOLISM
  • TUMOR-SUPPRESSOR
  • MODELS
  • CELLS

Inference of Cancer Mechanisms through Computational Systems Analysis

Tools:

Journal Title:

Molecular BioSystems

Volume:

Volume 13, Number 3

Publisher:

, Pages 489-497

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Large amounts of metabolomics data have been accumulated to study metabolic alterations in cancer that allow cancer cells to synthesize molecular materials necessary for cell growth and proliferation. Although metabolic reprogramming in cancer was discovered almost a century ago, the underlying biochemical mechanisms are still unclear. We show that metabolomics data can be used to infer likely biochemical mechanisms associated with cancer. The proposed inference method is data-driven and quite generic; its efficacy is demonstrated by the analysis of changes in purine metabolism of human renal cell carcinoma. The method and results are essentially unbiased and tolerate noise in the data well. The proposed method correctly identified and accurately quantified primary enzymatic alterations in cancer, and these account for over 80% of the metabolic alterations in the investigated carcinoma. Interestingly, the two primary action sites are not the most sensitive reaction steps in purine metabolism, which implies that sensitivity analysis is not a valid approach for identifying cancer targets. The proposed method exhibits statistically high precision and robustness even for analyses of moderately incomplete metabolomics data. By permitting analyses of individual metabolic profiles, the method may become a tool of personalized precision medicine.

Copyright information:

© The Royal Society of Chemistry.

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