Graph complexity analysis identifies an ETV5 tumor-specific network in human and murine low-grade glioma.

ABSTRACT: Conventional differential expression analyses have been successfully employed to identify genes whose levels change across experimental conditions. One limitation of this approach is the inability to discover central regulators that control gene expression networks. In addition, while methods for identifying central nodes in a network are widely implemented, the bioinformatics validation process and the theoretical error estimates that reflect the uncertainty in each step of the analysis are rarely considered. Using the betweenness centrality measure, we identified Etv5 as a potential tissue-level regulator in murine neurofibromatosis type 1 (Nf1) low-grade brain tumors (optic gliomas). As such, the expression of Etv5 and Etv5 target genes were increased in multiple independently-generated

SUBMITTER: Pan Y 

PROVIDER: S-EPMC5963759 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature