Abstract

Medulloblastoma is the most common type of solid brain tumor in children. This type of embryonic tumor is highly heterogeneous and has been classified into 4 molecular subgroups based on their gene expression profiles: WNT, SHH, Group 3 (G3) and Group 4 (G4). WNT and SHH tumors exhibit the specific dysregulation of genes and pathways, whereas G3 and G4 tumors, two of the more frequent subtypes, are the least characterized. Thus, novel markers to aid in the diagnosis, prognosis and management of medulloblastoma are required. In the present study, microarray gene expression data was downloaded from the Gene Expression Omnibus database, including data from the 4 subgroups of medulloblastoma and healthy cerebellum tissue (CT). The data was utilized in an in silico analysis to characterize each subgroup at a transcriptomic level. Using Partek Genomics Suite software, the data were visualized via hierarchical clustering and principal component analysis. The differentially expressed genes were uploaded to the MetaCore portal to perform enrichment analysis using CT gene expression as baseline, with fold change thresholds of <-5 and >5 for differential expression. The data mining analysis of microarray gene expression data enabled the identification of a range of dysregulated molecules associated with each subgroup of medulloblastoma. G4 is the most heterogeneous subgroup, as no definitive pathway defines its pathogenesis; analysis of the gene expression profiles were associated with the G4α and G4β subcategories. TOX high mobility group box family member 3, synuclein α interacting protein and, potassium voltage-gated channel interacting protein 4 were identified as three novel potential markers for distinguishing the α and β subcategories of G4. These genes may be associated with medulloblastoma pathogenesis, and thus may provide a basis for researching novel targeted treatment strategies for G4 medulloblastoma.

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