A systems biology-based gene expression classifier of glioblastoma predicts survival with solid tumors

Zhang, Jing, Liu, Bing, Jiang, Xingpeng, Zhao, Huizhi, Fan, Ming, Fan, Zhenjie, Lee, Jack J., Jiang, Tao, Jiang, Tianzi and Song, Sonya Wei (2009) A systems biology-based gene expression classifier of glioblastoma predicts survival with solid tumors. PLoS One, 4 7: . doi:10.1371/journal.pone.0006274


Author Zhang, Jing
Liu, Bing
Jiang, Xingpeng
Zhao, Huizhi
Fan, Ming
Fan, Zhenjie
Lee, Jack J.
Jiang, Tao
Jiang, Tianzi
Song, Sonya Wei
Title A systems biology-based gene expression classifier of glioblastoma predicts survival with solid tumors
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2009-07-17
Sub-type Article (original research)
DOI 10.1371/journal.pone.0006274
Open Access Status DOI
Volume 4
Issue 7
Total pages 13
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Language eng
Abstract Accurate prediction of survival of cancer patients is still a key open problem in clinical research. Recently, many large-scale gene expression clusterings have identified sets of genes reportedly predictive of prognosis; however, those gene sets shared few genes in common and were poorly validated using independent data. We have developed a systems biology-based approach by using either combined gene sets and the protein interaction network (Method A) or the protein network alone (Method B) to identify common prognostic genes based on microarray gene expression data of glioblastoma multiforme and compared with differential gene expression clustering (Method C). Validations of prediction performance show that the 23-prognostic gene classifier identified by Method A outperforms other gene classifiers identified by Methods B and C or previously reported for gliomas on 17 of 20 independent sample cohorts across five tumor types. We also find that among the 23 genes are 21 related to cellular proliferation and two related to response to stress/immune response. We further find that the increased expression of the 21 genes and the decreased expression of the other two genes are associated with poorer survival, which is supportive with the notion that cellular proliferation and immune response contribute to a significant portion of predictive power of prognostic classifiers. Our results demonstrate that the systems biology-based approach enables to identify common survival-associated genes.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article # e6274

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
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Created: Fri, 21 Oct 2011, 11:25:22 EST by Debra McMurtrie on behalf of Queensland Brain Institute