Identification of hub genes and potential molecular mechanisms in medulloblastoma by integrated bioinformatics analysis

Abstract

Abstract Background: Medulloblastoma (MB) is the most prevalent malignant brain cancer in children. Alterations in genes are critical in the molecular pathogenesis of medulloblastoma. Herein, we propose to explore the potential biomarkers for medulloblastoma and further elucidate their possible molecular mechanism. Methods: Medulloblastoma data sets were abstracted from the GEO (Gene Expression Omnibus) data resource and merged with DEGs (differentially expressed genes) from individual microarrays. Then, GO (Gene Ontology) along with KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses were employed to analyze the data set. Results: We selected 3 GEO data sets of 15 normal cerebellum tissues and 66 medulloblastoma tissues. Overall, 47 DEGs were identified. Of these, 26 were upregulated, whereas 21 were downregulated DEGs. Based on the GO analysis, the primary biological focus of DEGs consisted of “Positive regulation of neuron “differentiation,” “Intracellular,” as well as “Calcium ion binding.” The most remarkable cascade based on the KEGG analysis was the “P53 signaling cascade”. The PPI network demonstrated DTL, MELK, CDK1, KIF11, NDC80, PBK, NUSAP1, TOP2A, TTK, and RRM2 as the most remarkable hub genes. Conclusion: Identification of the most remarkable hub genes and significant cascades in medulloblastoma provides critical information on the pathogenesis of medulloblastoma. In addition, this information can be used to identify diagnostic biomarkers.