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BioMed Research International
Volume 2013 (2013), Article ID 303982, 11 pages
http://dx.doi.org/10.1155/2013/303982
Research Article

A Machine-Learned Predictor of Colonic Polyps Based on Urinary Metabolomics

1Department of Computing Science, University of Alberta, Edmonton, AB, Canada T6G 2E8
2Division of Gastroenterology, Zeidler Ledcor Centre, University of Alberta, Edmonton, AB, Canada T6G 2X8
3Department of Surgery, 2D2.29 WC Mackenzie Health Science Centre, University of Alberta, Edmonton, AB, Canada T6G 2R7

Received 10 July 2013; Revised 29 August 2013; Accepted 8 September 2013

Academic Editor: Yudong Cai

Copyright © 2013 Roman Eisner et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. International Agency for Research on Cancer and World Health Organization, “Estimated cancer Incidence, mortality, prevalence and disability-adjusted life years (DALYs) worldwide,” GLOBOCAN 2008, International Agency for Research on Cancer, Lyon, France, 2008.
  2. D. M. Parkin, F. Bray, J. Ferlay, and P. Pisani, “Global cancer statistics, 2002,” CA: A Cancer Journal for Clinicians, vol. 55, no. 2, pp. 74–108, 2005. View at Scopus
  3. “Colon and rectal cancer,” National Cancer Institute http://www.cancer.gov/cancertopics/types/colon-and-rectal.
  4. Canadian Cancer Society's Steering Committee on Cancer Statistics, Canadian Cancer Statistics, Toronto, Canada, 2011.
  5. D. J. Leddin, R. Enns, R. Hilsden et al., “Canadian Association of Gastroenterology position statement on screening individuals at average risk for developing colorectal cancer: 2010,” Canadian Journal of Gastroenterology, vol. 24, no. 12, pp. 705–714, 2010. View at Scopus
  6. D. P. Taylor, L. A. Cannon-Albright, C. Sweeney et al., “Comparison of compliance for colorectal cancer screening and surveillance by colonoscopy based on risk,” Genetics in Medicine, vol. 13, no. 8, pp. 737–743, 2011. View at Publisher · View at Google Scholar · View at Scopus
  7. C. K. W. Wong, R. N. Fedorak, C. I. Prosser, M. E. Stewart, S. V. van Zanten, and D. C. Sadowski, “The sensitivity and specificity of guaiac and immunochemical fecal occult blood tests for the detection of advanced colonic adenomas and cancer,” International Journal of Colorectal Disease, vol. 27, no. 12, pp. 1657–1664, 2012. View at Publisher · View at Google Scholar
  8. J. E. Allison, I. S. Tekawa, L. J. Ransom, and A. L. Adrain, “A comparison of fecal occult-blood tests for colorectal-cancer screening,” The New England Journal of Medicine, vol. 334, no. 3, pp. 155–159, 1996. View at Publisher · View at Google Scholar · View at Scopus
  9. T. F. Imperiale, D. F. Ransohoff, S. H. Itzkowitz, B. A. Turnbull, and M. E. Ross, “Fecal DNA versus fecal occult blood for colorectal-cancer screening in an average-risk population,” The New England Journal of Medicine, vol. 351, no. 26, pp. 2704–2714, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. E. P. Whitlock, J. S. Lin, E. Liles, T. L. Beil, and R. Fu, “Screening for colorectal cancer: a targeted, updated systematic review for the U.S. Preventive Services Task Force,” Annals of Internal Medicine, vol. 149, no. 9, pp. 638–658, 2008. View at Scopus
  11. J. Maroun, E. Ng, J.-M. Berthelot et al., “Lifetime costs of colon and rectal cancer management in Canada,” Chronic Diseases in Canada, vol. 24, no. 4, pp. 91–101, 2003. View at Scopus
  12. D. S. Wishart, T. Jewison, A. C. Guo, et al., “HMDB 3.0—the human metabolome database in 2013,” Nucleic Acids Research, vol. 41, pp. D801–D807, 2013. View at Publisher · View at Google Scholar
  13. A. M. Weljie, J. Newton, P. Mercier, E. Carlson, and C. M. Slupsky, “Targeted pofiling: quantitative analysis of 1H-NMR metabolomics data,” Analytical Chemistry, vol. 78, no. 13, pp. 4430–4442, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. D. S. Wishart, “Quantitative metabolomics using NMR,” TrAC—Trends in Analytical Chemistry, vol. 27, no. 3, pp. 228–237, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Eisner, C. Stretch, T. Eastman et al., “Learning to predict cancer-associated skeletal muscle wasting from 1H-NMR profiles of urinary metabolites,” Metabolomics, vol. 7, no. 1, pp. 25–34, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Stretch, T. Eastman, R. Mandal et al., “Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach,” Journal of Nutrition, vol. 142, no. 1, pp. 14–21, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Xia, N. Psychogios, N. Young, and D. S. Wishart, “MetaboAnalyst: a web server for metabolomic data analysis and interpretation,” Nucleic Acids Research, vol. 37, no. 2, pp. W652–W660, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Holmes, P. J. D. Foxall, J. K. Nicholson et al., “Automatic data reduction and pattern recognition methods for analysis of 1H nuclear magnetic resonance spectra of human urine from normal and pathological states,” Analytical Biochemistry, vol. 220, no. 2, pp. 284–296, 1994. View at Publisher · View at Google Scholar · View at Scopus
  19. F. Dieterle, A. Ross, G. Schlotterbeck, and H. Senn, “Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H-NMR metabonomics,” Analytical Chemistry, vol. 78, no. 13, pp. 4281–4290, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. M. E. Bollard, E. G. Stanley, J. C. Lindon, J. K. Nicholson, and E. Holmes, “NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition,” NMR in Biomedicine, vol. 18, no. 3, pp. 143–162, 2005. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Craig, O. Cloarec, E. Holmes, J. K. Nicholson, and J. C. Lindon, “Scaling and normalization effects in NMR spectroscopic metabonomic data sets,” Analytical Chemistry, vol. 78, no. 7, pp. 2262–2267, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. J. L. Spratlin, N. J. Serkova, and S. G. Eckhardt, “Clinical applications of metabolomics in oncology: a review,” Clinical Cancer Research, vol. 15, no. 2, pp. 431–440, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. R. Madsen, T. Lundstedt, and J. Trygg, “Chemometrics in metabolomics—a review in human disease diagnosis,” Analytica Chimica Acta, vol. 659, no. 1-2, pp. 23–33, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  25. E. Frank, M. Hall, G. Holmes, et al., “Weka-A machine learning workbench for data mining,” in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds., pp. 1269–1277, Springer, 2nd edition, 2010. View at Publisher · View at Google Scholar
  26. R. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society B, vol. 58, pp. 267–288, 1996.
  27. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, NY, USA, 2001.
  28. F. Provost and T. Fawcett, Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions, AAAI Press, 1997.
  29. M. J. J. Scott, M. Niranjan, and R. W. Prager, “Realisable classifiers: improving operating performance on variable cost problems,” in Proceedings of 9th British Machine Vision Conferenc, pp. 304–315, University of Southampton, Southampton , UK, 1998.
  30. S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Upper Saddle River, NJ, USA, 2002.
  31. C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” Journal of Bioinformatics and Computational Biology, vol. 3, no. 2, pp. 185–205, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. T. Huang, S. Wan, Z. Xu et al., “Analysis and prediction of translation rate based on sequence and functional features of the mRNA,” PLoS ONE, vol. 6, no. 1, Article ID e16036, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Sun, Y. Yu, T. Huang, et al., “Associations between ionomic profile and metabolic abnormalities in human population,” PLoS ONE, vol. 7, no. 6, Article ID e38845, 2012. View at Publisher · View at Google Scholar
  34. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. F. Pesarin, Multivariate Permutation Tests: With Applications in Biostatistics, John Wiley & Sons, Chichester, UK, 2001.
  36. Y. Qiu, G. Cai, M. Su et al., “Serum metabolite profiling of human colorectal cancer using GC-TOFMS and UPLC-QTOFMS,” Journal of Proteome Research, vol. 8, no. 10, pp. 4844–4850, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Cheng, G. Xie, T. Chen et al., “Distinct urinary metabolic profile of human colorectal cancer,” Journal of Proteome Research, vol. 11, no. 2, pp. 1354–1363, 2012. View at Publisher · View at Google Scholar · View at Scopus
  38. W. Wang, B. Feng, X. Li et al., “Urinary metabolic profiling of colorectal carcinoma based on online affinity solid phase extraction-high performance liquid chromatography and ultra performance liquid chromatography-mass spectrometry,” Molecular BioSystems, vol. 6, no. 10, pp. 1947–1955, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Wang, D. E. Schiller, V. Tso, C. Slupsky, C. K. Wong, and R. N. Fedorak, “A novel highly sensitive test for detecting colon cancer using spot urine metabolomics,” Gastroenterology, vol. 140, no. 5, supplement 1, p. S40. View at Publisher · View at Google Scholar
  40. Y. Qiu, G. Cai, M. Su et al., “Urinary metabonomic study on colorectal cancer,” Journal of Proteome Research, vol. 9, no. 3, pp. 1627–1634, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. S. Nishiumi, T. Kobayashi, A. Ikeda, et al., “A novel serum metabolomics-based diagnostic approach for colorectal cancer,” PLoS ONE, vol. 7, no. 7, Article ID e40459, 2012. View at Publisher · View at Google Scholar
  42. M. Mal, P. K. Koh, P. Y. Cheah, and E. C. Y. Chan, “Metabotyping of human colorectal cancer using two-dimensional gas chromatography mass spectrometry,” Analytical and Bioanalytical Chemistry, vol. 403, no. 2, pp. 483–493, 2012. View at Publisher · View at Google Scholar · View at Scopus
  43. Y. Ma, P. Zhang, F. Wang, W. Liu, J. Yang, and H. Qin, “An integrated proteomics and metabolomics approach for defining oncofetal biomarkers in the colorectal cancer,” Annals of Surgery, vol. 255, no. 4, pp. 720–730, 2012. View at Publisher · View at Google Scholar · View at Scopus
  44. H. Wang, V. K. Tso, C. M. Slupsky, and R. N. Fedorak, “Metabolomics and detection of colorectal cancer in humans: a systematic review,” Future Oncology, vol. 6, no. 9, pp. 1395–1406, 2010. View at Publisher · View at Google Scholar · View at Scopus
  45. F. Farshidfar, A. M. Weljie, K. Kopciuk, et al., “Serum metabolomic profile as a means to distinguish stage of colorectal cancer,” Genome Medicine, vol. 4, article 42, 2012. View at Publisher · View at Google Scholar
  46. Y. Miyagi, M. Higashiyama, A. Gochi et al., “Plasma free amino acid profiling of five types of cancer patients and its application for early detection,” PLoS ONE, vol. 6, no. 9, Article ID e24143, 2011. View at Publisher · View at Google Scholar · View at Scopus
  47. A. Arlt, S. Sebens, S. Krebs, et al., “Inhibition of the Nrf2 transcription factor by the alkaloid trigonelline renders pancreatic cancer cells more susceptible to apoptosis through decreased proteasomal gene expression and proteasome activity,” Oncogene, 2012. View at Publisher · View at Google Scholar