GBM Molecular Subtypes Protocol

The second GBM dataset analyzed for the present study is available as supplementary material of a recent study[27 (link)]. Data include microarray expression profiles of 173 core TCGA samples unified and scaled from three gene expression platforms (Affymetrix HuEx array, Affymetrix U133A array, and Agilent 244K array). The authors of this study described a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, PDGFRA/IDH1, and neuron markers (e.g. NEFL, GABRA1, SYT1, SLC12A5) each define the Classical, Mesenchymal, Proneural and Neural subtypes, respectively.

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Fiscon G., Conte F, & Paci P. (2018). SWIM tool application to expression data of glioblastoma stem-like cell lines, corresponding primary tumors and conventional glioma cell lines. BMC Bioinformatics, 19(Suppl 15), 436.

Publication 2018

HuEx array U133A array 244K array

A gene Egfr gene Gene expression Genomic Mesenchymal Microarray Mutations Nefl Neural Neuron Somatic Syt1

Corresponding Organization :

Other organizations : Istituto di Analisi dei Sistemi ed Informatica Antonio Ruberti, National Research Council

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Variable analysis

independent variables

Molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes

dependent variables

Microarray expression profiles of 173 core TCGA samples
Somatic mutations and DNA copy number

control variables

Three gene expression platforms (Affymetrix HuEx array, Affymetrix U133A array, and Agilent 244K array) used to unify and scale the data

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