Identification of transcriptional regulatory networks specific to pilocytic astrocytoma

Hrishikesh Deshmukh,Jacqueline E Payton,Jahangheer Shaik,David H Gutmann,Mark A Watson,Arie Perry,Rakesh Nagarajan,Jinsheng Yu,Tobey J Macdonald

doi:10.1186/1755-8794-4-57

Hrishikesh Deshmukh, Jacqueline E Payton + Show 7 more

Open Access

https://doi.org/10.1186/1755-8794-4-57

Copy DOI

Abstract

BackgroundPilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours.MethodsRNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization.ResultsIn this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions.ConclusionsThese results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer.

Highlights

Pilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified
Because cell of origin is not known for these tumor types, we could not use in vivo or in vitro derived normal cells as a comparison
In the current study, we employed only transcription factors (TFs)-gene interactions to assemble predicted regulatory networks. The use of this methodological approach illustrates how traditional, microarray-based gene expression data can be integrated with a novel TF-gene interaction database generated from primary genome sequence analysis to reveal higher-order, cell-specific transcriptional regulatory networks

Summary

Introduction

Pilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours. In central nervous system (CNS) tumors, array-based comparative genomic hybridization (aCGH) and DNA sequencing have led to the. Mutation, altered copy number, and abnormal expression of single genes observed to vary across individual tumors may be better viewed collectively to identify underlying commonalities at the level of molecular programs, based on known protein-protein interactions, canonical cell signaling pathways, and in silico transcriptional regulatory control predictions. As in other reported studies, only a limited number of consistent genetic alterations were identified

Methods

Results

Conclusion