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Disease Markers
Volume 2017 (2017), Article ID 5745724, 7 pages
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

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.


Background. Lymph node (LN) metastasis was an independent risk factor for stomach cancer recurrence, and the presence of LN metastasis has great influence on the overall survival of stomach cancer patients. Thus, accurate prediction of the presence of lymph node metastasis can provide guarantee of credible prognosis evaluation of stomach cancer patients. Recently, increasing evidence demonstrated that the aberrant DNA methylation first appears before symptoms of the disease become clinically apparent. Objective. Selecting key biomarkers for LN metastasis presence prediction for stomach cancer using clinical DNA methylation based on a machine learning method. Methods. To reduce the overfitting risk of prediction task, we applied a three-step feature selection method according to the property of DNA methylation data. Results. The feature selection procedure extracted several cancer-related and lymph node metastasis-related genes, such as TP73, PDX1, FUT8, HOXD1, NMT1, and SEMA3E. The prediction performance was evaluated on the public DNA methylation dataset. The results showed that the three-step feature procedure can largely improve the prediction performance and implied the reliability of the biomarkers selected. Conclusions. With the selected biomarkers, the prediction method can achieve higher accuracy in detecting LN metastasis and the results also proved the reliability of the selected biomarkers indirectly.