title: Identification and characterization of alternative exon usage linked glioblastoma multiforme survival.
abstract: BACKGROUND
  Alternative exon usage (AEU) is an important component of gene regulation. Exon expression platforms allow the detection of associations between AEU and phenotypes such as cancer. Numerous studies have identified associations between gene expression and the brain cancer glioblastoma multiforme (GBM). The few consistent gene expression biomarkers of GBM that have been reported may be due to the limited consideration of AEU and the analytical approaches used. The objectives of this study were to develop a model that accounts for the variations in expression present between the exons within a gene and to identify AEU biomarkers of GBM survival.
  
  
  METHODS
  The expression of exons corresponding to 25,403 genes was related to the survival of 250 individuals diagnosed with GBM in a training data set. Genes exhibiting AEU in the training data set were confirmed in an independent validation data set of 78 patients. A hierarchical mixed model that allows the consideration of covariation between exons within a gene and of the effect of the epidemiological characteristics of the patients was developed to identify associations between exon expression and patient survival. This general model describes all three possible scenarios: multi-exon genes with and without AEU, and single-exon genes.
  
  
  RESULTS
  AEU associated with GBM survival was identified on 2477 genes (P-value < 5.0E-04 or FDR-adjusted P-value < 0.05). G-protein coupled receptor 98 (Gpr98) and epidermal growth factor (Egf) were among the genes exhibiting AEU with 30 and 9 exons associated with GBM survival, respectively. Pathways enriched among the AEU genes included focal adhesion, ECM-receptor interaction, ABC transporters and pathways in cancer. In addition, 24 multi-exon genes without AEU and 8 single-exon genes were associated with GBM survival (FDR-adjusted P-value < 0.05).
  
  
  CONCLUSIONS
  The inferred patterns of AEU were consistent with in silico AS models. The hierarchical model used offered a flexible and simple way to interpret and identify associations between survival that accommodates multi-exon genes with or without AEU and single exon genes. Our results indicate that differential expression of AEU could be used as biomarker for GBM and potentially other cancers.
authors.affiliation: Department of Animal Sciences, University of Illinois Urbana-Champaign, Urbana, IL, USA.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Sadeque
authors.forename: Nicola Vl
authors.initials: NV
authors.lastname: Serão
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Southey
authors.forename: Kristin R
authors.initials: KR
authors.lastname: Delfino
authors.forename: Sandra L
authors.initials: SL
authors.lastname: Rodriguez-Zas
mesh_terms: D017398:Alternative Splicing; D004815:Epidermal Growth Factor / Q000235:genetics / Q000378:metabolism; D005091:Exons; D005260:Female; D015894:Genome, Human; D005909:Glioblastoma / Q000235:genetics* / Q000401:mortality; D006801:Humans; D008297:Male; D008957:Models, Genetic; D016017:Odds Ratio; D020411:Oligonucleotide Array Sequence Analysis; D043562:Receptors, G-Protein-Coupled / Q000235:genetics / Q000378:metabolism; D016019:Survival Analysis
chemical_list: C417525:ADGRV1 protein, human; D043562:Receptors, G-Protein-Coupled; D004815:Epidermal Growth Factor