Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data.

ABSTRACT: Histopathologic grading of astrocytic tumors based on current WHO criteria offers a valuable but simplified representation of oncologic reality and is often insufficient to predict clinical outcome. In this study, we report a new astrocytic tumor microarray gene expression data set (n = 65). We have used a simple artificial neural network algorithm to address grading of human astrocytic tumors, derive specific transcriptional signatures from histopathologic subtypes of astrocytic tumors, and asses whether these molecular signatures define survival prognostic subclasses. Fifty-nine classifier genes were identified and found to fall within three distinct functional classes, that is, angiogenesis, cell differentiation, and lower-grade astrocytic tumor discrimination. These gene classes were f

SUBMITTER: Petalidis LP 

PROVIDER: S-EPMC2819720 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature