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

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 c

SUBMITTER: Zhang J 

PROVIDER: S-EPMC2707631 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature