Examining the biomarkers and molecular mechanisms of medulloblastoma based on bioinformatics analysis.

Abstract

Medulloblastoma (MB) is the most common malignant brain tumor in children. The aim of the present study was to predict biomarkers and reveal their potential molecular mechanisms in MB. The gene expression profiles of GSE35493, GSE50161, GSE74195 and GSE86574 were downloaded from the Gene Expression Omnibus (GEO) database. Using the Limma package in R, a total of 1,006 overlapped differentially expressed genes (DEGs) with the cut-off criteria of P<0.05 and |log2fold-change (FC)|>1 were identified between MB and normal samples, including 540 upregulated and 466 downregulated genes. Furthermore, the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were also performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool to analyze functional and pathway enrichment. The Search Tool for Retrieval of Interacting Genes database was subsequently used to construct a protein-protein interaction (PPI) network and the network was visualized in Cytoscape. The top 11 hub genes, including CDK1, CCNB1, CCNB2, PLK1, CDC20, MAD2L1, AURKB, CENPE, TOP2A, KIF2C and PCNA, were identified from the PPI network. The survival curves for hub genes in the dataset GSE85217 predicted the association between the genes and survival of patients with MB. The top 3 modules were identified by the Molecular Complex Detection plugin. The results indicated that the pathways of DEGs in module 1 were primarily enriched in cell cycle, progesterone-mediated oocyte maturation and oocyte meiosis; and the most significant functional pathways in modules 2 and 3 were primarily enriched in mismatch repair and ubiquitin-mediated proteolysis, respectively. These results may help elucidate the pathogenesis and design novel treatments for MB.