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Series GSE2817 Query DataSets for GSE2817
Status Public on Dec 15, 2005
Title Wavelet modelling of microarray data provides chromosomal pattern of expression which predicts survival in gliomas
Organism Homo sapiens
Experiment type Expression profiling by array
Summary Genetic and epigenetic processes result in gene expression changes through alteration of the chromatin structure. The relative position of genes on chromosomes has therefore important functional implications and can be exploited to model microarray datasets. Gliomas are the most frequent primary brain tumours in adults and their prognosis is related to histology and grade. In oligodendrogliomas, allelic loss of 1p/19q and hypermethylation of MGMT promoter is associated with longer survival and chemosensitivity. In this work we used oligonucleotide microarray to study a group of 30 gliomas with various oligodendroglial and astrocytic components. We used an original approach combining a wavelet model of inter-probe genomic distance (CHROMOWAVE) and unsupervised method of analysis (Singular Value Decomposition) in order to discover new prognostic chromosomal patterns of gene expression. We identified a major pattern of variation that strongly correlated with survival (p= 0.007) and could be visualized as a genome-wide chromosomal pattern including widespread gene expression changes on 1p, 19q, 4, 18, 13 and 9q and multiple smaller clusters scattered along chromosomes. Gene expression changes on chromosomes 1p, 19q and 9q were significantly correlated with the allelic loss of these regions as measured by FISH. Differential expression of genes implicated in drug resistance was also a feature of this chromosomal pattern and in particular low expression of MGMT was correlated with favourable prognosis (p<0.0001). Remarkably, unsupervised analysis of the expression of individual genes and not of their chromosomal ensemble produced a pattern that could not be associated with prognosis, emphasizing the determinant role of the wavelet mathematical modelling.
Keywords: wavelet, glioma, unsupervised
 
Overall design Unsupervised analysis using wavelet models of 30 diffuse gliomas
 
Contributor(s) Deprez M, Hennuy B, Herens C, Roncaroli F, Nguyen M, Martin D, Bours V, Boniver J, Turkheimer F
Citation(s) 17140431
Submission date Jun 15, 2005
Last update date Mar 25, 2019
Contact name Federico Turkheimer
E-mail(s) federico.turkheimer@imperial.ac.uk
Phone +44 208 383 7051
Fax +44 208 383 2029
Organization name Imperial College London
Department Sensorimotor
Street address DuCane Road
City London
ZIP/Postal code W12 0NN
Country United Kingdom
 
Platforms (1)
GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
Samples (30)
GSM60958 AA3
GSM60959 AA5
GSM60960 AA6
Relations
BioProject PRJNA92371

Download family Format
SOFT formatted family file(s) SOFTHelp
MINiML formatted family file(s) MINiMLHelp
Series Matrix File(s) TXTHelp

Supplementary file Size Download File type/resource
GSE2817_RAW.tar 235.2 Mb (http)(custom) TAR (of CEL, EXP)

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