Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017, Article ID 3017948, 10 pages
Research Article

Joint Covariate Detection on Expression Profiles for Selecting Prognostic miRNAs in Glioblastoma

College of Information and Computer Engineering, Northeast Forestry University, Harbin 150001, China

Correspondence should be addressed to Xudong Zhao; nc.ude.ufen@gnoduxoahz

Received 4 October 2016; Revised 18 January 2017; Accepted 27 February 2017; Published 20 March 2017

Academic Editor: Xia Li

Copyright © 2017 Chengqi Sun and Xudong Zhao. 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.


An important application of expression profiles is to stratify patients into high-risk and low-risk groups using limited but key covariates associated with survival outcomes. Prior to that, variables considered to be associated with survival outcomes are selected. A combination of single variables, each of which is significantly related to survival outcomes, is always regarded to be candidates for posterior patient stratification. Instead of individually significant variables, a combination that contains not only significant but also insignificant variables is supposed to be concentrated on. By means of bottom-up enumeration on each pair of variables, we propose a joint covariate detection strategy to select candidates that not only correspond to close association with survival outcomes but also help to make a clear stratification of patients. Experimental results on a publicly available dataset of glioblastoma multiforme indicate that the selected pair composed of an individually significant and an insignificant miRNA keeps a better performance than the combination of significant single variables. The selected miRNA pair is ultimately regarded to be associated with the prognosis of glioblastoma multiforme by further pathway analysis.