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Disease Markers
Volume 2017 (2017), Article ID 5745724, 7 pages
https://doi.org/10.1155/2017/5745724
Research Article

Identification of Biomarkers for Predicting Lymph Node Metastasis of Stomach Cancer Using Clinical DNA Methylation Data

1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
2Department of Automation, Shanghai Jiao Tong University, Shanghai, China
3School of Communications and Electronics, Jiangxi Science & Technology Normal University, Nanchang, China
4Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

Correspondence should be addressed to Xiaodong Zhao; moc.oohay@221gnodoaix

Received 25 May 2017; Revised 14 July 2017; Accepted 24 July 2017; Published 29 August 2017

Academic Editor: Yuen Yee Cheng

Copyright © 2017 Jun Wu 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. J. Ferlay, I. Soerjomataram, R. Dikshit et al., “Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012,” International Journal of Cancer, vol. 136, no. 5, pp. E359–E386, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Antoni, I. Soerjomataram, B. Møller, F. Bray, and J. Ferlay, “An assessment of GLOBOCAN methods for deriving national estimates of cancer incidence,” Methods, vol. 3, p. 4, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. E. Rubin and J. P. Palazzo, “The gastrointestinal tract,” in Rubin’s Pathology. Clinicopathologic Foundations of Medicine, pp. 660–739, Lippincott Williams & Wilkins, Philadelphia, 4th edition, 2005. View at Google Scholar
  4. T. Akagi, N. Shiraishi, and S. Kitano, “Lymph node metastasis of gastric cancer,” Cancer, vol. 3, no. 2, pp. 2141–2159, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. D. Park, Y. J. Chung, H. Y. Chung et al., “Factors related to lymph node metastasis and the feasibility of endoscopic mucosal resection for treating poorly differentiated adenocarcinoma of the stomach,” Endoscopy, vol. 40, no. 1, pp. 7–10, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. R. M. Kwee and T. C. Kwee, “Predicting lymph node status in early gastric cancer,” Gastric Cancer, vol. 11, no. 3, pp. 134–148, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Y. Deng and H. Liang, “Clinical significance of lymph node metastasis in gastric cancer,” World Journal of Gastroenterology, vol. 20, no. 14, pp. 3967–3975, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Isozaki, K. Okajima, E. Nomura et al., “Preoperative diagnosis and surgical treatment for LN metastasis in gastric cancer (in Japanese),” Gan to Kagaku Ryoho, vol. 23, pp. 1275–1283, 1996. View at Google Scholar
  9. S. B. Baylin and J. G. Herman, “DNA alterations in cancer: genetic and epigenetic alterations,” in DNA Alterations in Cancer, pp. 293–309, Eaton Publishing, Natick, 2000. View at Google Scholar
  10. M. Ehrlich, “DNA methylation in cancer: too much, but also too little,” Oncogene, vol. 21, no. 35, pp. 5400–5413, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Bergman and H. Cedar, “DNA methylation dynamics in health and disease,” Nature Structural & Molecular Biology, vol. 20, no. 3, pp. 274–281, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. S. B. Baylin, S. A. Belinsky, and J. G. Herman, “Aberrant methylation of gene promoters in cancer—concepts, misconcepts, and promise,” Journal of the National Cancer Institute, vol. 92, no. 18, pp. 1460-1461, 2000. View at Publisher · View at Google Scholar
  13. K. M. Godfrey, A. Sheppard, P. D. Gluckman et al., “Epigenetic gene promoter methylation at birth is associated with child’s later adiposity,” Diabetes, vol. 60, no. 5, pp. 1528–1534, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. A. Jones, A. E. Teschendorff, Q. Li et al., “Role of DNA methylation and epigenetic silencing of HAND2 in endometrial cancer development,” PLoS Medicine, vol. 10, no. 11, article e1001551, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. W. A. Palmisano, K. K. Divine, G. Saccomanno et al., “Predicting lung cancer by detecting aberrant promoter methylation in sputum,” Cancer Research, vol. 60, no. 21, pp. 5954–5958, 2000. View at Google Scholar
  16. P. Adorján, J. Distler, E. Lipscher et al., “Tumour class prediction and discovery by microarray-based DNA methylation analysis,” Nucleic Acids Research, vol. 30, no. 5, article e21, 2002. View at Publisher · View at Google Scholar
  17. F. J. Carmona, D. Azuara, A. Berenguer-Llergo et al., “DNA methylation biomarkers for noninvasive diagnosis of colorectal cancer,” Cancer Prevention Research, vol. 6, no. 7, pp. 656–665, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Fukushige and A. Horii, “DNA methylation in cancer: a gene silencing mechanism and the clinical potential of its biomarkers,” The Tohoku Journal of Experimental Medicine, vol. 229, no. 3, pp. 173–185, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Hijazi and C. Chan, “A classification framework applied to cancer gene expression profiles,” Journal of Healthcare Engineering, vol. 4, no. 2, pp. 255–283, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Bi, K. Bennett, and M. Embrechts, “Dimensionality reduction via sparse support vector machines,” Journal of Machine Learning Research, vol. 3, pp. 1229–1243, 2003. View at Google Scholar
  21. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1, pp. 273–324, 1997. View at Publisher · View at Google Scholar
  22. S. Perkins, K. Lacker, and J. Theiler, “Grafting: fast, incremental feature selection by gradient descent in function space,” Journal of Machine Learning Research, vol. 3, pp. 1333–1356, 2003. View at Google Scholar
  23. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  24. X. M. Zhao, Y. M. Cheung, and D. S. Huang, “A novel approach to extracting features from motif content and protein composition for protein sequence classification,” Neural Networks, vol. 18, no. 8, pp. 1019–1028, 2005. View at Publisher · View at Google Scholar · View at Scopus
  25. H. Q. Wang, D. S. Huang, and B. Wang, “Optimisation of radial basis function classifiers using simulated annealing algorithm for cancer classification,” Electronics Letters, vol. 41, no. 11, pp. 630–632, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. K. H. Chen, K. J. Wang, M. L. Tsai et al., “Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm,” BMC Bioinformatics, vol. 15, no. 1, p. 1, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. T. Stiewe and B. M. Putzer, “Role of p73 in malignancy: tumor suppressor or oncogene?” Cell Death and Differentiation, vol. 9, no. 3, pp. 237–245, 2002. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Ma, M. Chen, J. Wang et al., “Pancreatic duodenal homeobox-1 (PDX1) functions as a tumor suppressor in gastric cancer,” Carcinogenesis, vol. 29, no. 7, pp. 1327–1333, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Ito, A. Miyauchi, H. Yoshida et al., “Expression of α1, 6-fucosyltransferase (FUT8) in papillary carcinoma of the thyroid: its linkage to biological aggressiveness and anaplastic transformation,” Cancer Letters, vol. 200, no. 2, pp. 167–172, 2003. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Bhatlekar, J. Z. Fields, and B. M. Boman, “HOX genes and their role in the development of human cancers,” Journal of Molecular Medicine, vol. 92, no. 8, pp. 811–823, 2014. View at Publisher · View at Google Scholar · View at Scopus
  31. “The TCGA Database,” http://cancergenome.nih.gov/.