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BioMed Research International
Volume 2018 (2018), Article ID 2914280, 11 pages
https://doi.org/10.1155/2018/2914280
Research Article

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

1Cheonan Public Health Center, Chungnam, Republic of Korea
2Department of Community Health, Korea Health Promotion Institute, Seoul, Republic of Korea
3Department of Clinical Medical Sciences, Seoul National University, College of Medicine, Seoul, Republic of Korea

Correspondence should be addressed to Hongyoon Choi; moc.liamg@0001yhc and Kwon Joong Na; moc.liamg@58anjk

Received 13 October 2017; Revised 7 December 2017; Accepted 11 December 2017; Published 16 January 2018

Academic Editor: Jialiang Yang

Copyright © 2018 Hongyoon Choi and Kwon Joong Na. 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.

Abstract

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients’ survival independent of clinicopathological variables. Five networks were significantly associated with patients’ survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients’ survival in two test sets and training set (, and for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients’ prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction.