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

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• 作者：Yohan Lee (1)
  Adrienne C Scheck (2)
  Timothy F Cloughesy (3) (6)
  Albert Lai (3)
  Jun Dong (5)
  Haumith K Farooqi
  Linda M Liau (4) (6)
  Steve Horvath (5)
  Paul S Mischel (6)
  Stanley F Nelson (1) (6)
  
• 刊名：BMC Medical Genomics
• 出版年：2008
• 出版时间：December 2008
• 年：2008
• 卷：1
• 期：1
• 全文大小：1487KB
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  25. The pre-publication history for this paper can be accessed here:f="http://www.biomedcentral.com/1755-8794/1/52/prepub" class="a-plus-plus">http://www.biomedcentral.com/1755-8794/1/52/prepub
  
• 作者单位：Yohan Lee (1)
  Adrienne C Scheck (2)
  Timothy F Cloughesy (3) (6)
  Albert Lai (3)
  Jun Dong (5)
  Haumith K Farooqi
  Linda M Liau (4) (6)
  Steve Horvath (5)
  Paul S Mischel (6)
  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
  6. Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California, 90095-1781, USA
  5. Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, 90095-1772, USA
  4. Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, 90095-6901, USA
  
• ISSN：1755-8794

文摘

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.