Research article

Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age

Yohan Lee¹ , Adrienne C Scheck² , Timothy F Cloughesy^3,6 , Albert Lai³ , Jun Dong⁵ , Haumith K Farooqi , Linda M Liau^4,6 , Steve Horvath⁵ , Paul S Mischel⁶ and Stanley F Nelson^1,6

¹  Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-7088 USA

²  The Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, Arizona 85013 USA

³  Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-1769 USA

⁴  Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-6901 USA

⁵  Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California 90095-1772 USA

⁶  Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California 90095-1781 USA

author email corresponding author email

BMC Medical Genomics 2008, 1:52doi:10.1186/1755-8794-1-52

Published:	21 October 2008

Abstract

Background

Glioblastomas are the most common primary brain tumour in adults. While the prognosis for patients is poor, gene expression profiling has detected signatures that can sub-classify GBMs relative to histopathology and clinical variables. One category of GBM defined by a gene expression signature is termed ProNeural (PN), and has substantially longer patient survival relative to other gene expression-based subtypes of GBMs. Age of onset is a major predictor of the length of patient survival where younger patients survive longer than older patients. The reason for this survival advantage has not been clear.

Methods

We collected 267 GBM CEL files and normalized them relative to other microarrays of the same Affymetrix platform. 377 probesets on U133A and U133 Plus 2.0 arrays were used in a gene voting strategy with 177 probesets of matching genes on older U95Av2 arrays. Kaplan-Meier curves and Cox proportional hazard analyses were applied in distinguishing survival differences between expression subtypes and age.

Results

This meta-analysis of published data in addition to new data confirms the existence of four distinct GBM expression-signatures. Further, patients with PN subtype GBMs had longer survival, as expected. However, the age of the patient at diagnosis is not predictive of survival time when controlled for the PN subtype.

Conclusion

The survival benefit of younger age is nullified when patients are stratified by gene expression group. Thus, the main cause of the age effect in GBMs is the more frequent occurrence of PN GBMs in younger patients relative to older patients.