Medulloblastoma (MB) is one of the most common intracranial malignancies in children. The present study applied integrated bioinformatics to identify potential core genes associated with the pathogenesis of MB and reveal potential molecular mechanisms. Through the integrated analysis of multiple data sets from the Gene Expression Omnibus (GEO), 414 differentially expressed genes (DEGs) were identified. Combining the protein–protein interaction (PPI) network analysis with gene set enrichment analysis (GSEA), eight core genes, including CCNA2, CCNB1, CCNB2, AURKA, CDK1, MAD2L1, BUB1B, and RRM2, as well as four core pathways, including “cell cycle”, “oocyte meiosis”, “p53 pathway” and “DNA replication” were selected. In independent data sets, the core genes showed superior diagnostic values and significant prognostic correlations. Moreover, in the pan-caner data of the cancer genome atlas (TCGA), the core genes were also widely abnormally expressed. In conclusion, this study identified core genes and pathways of MB through integrated analysis to deepen the understanding of the molecular mechanisms underlying the MB and provide potential targets and pathways for diagnosis and treatment of MB.

髓母细胞瘤（MB）是儿童最常见的颅内恶性肿瘤之一。本研究应用综合生物信息学来识别与 MB 发病机制相关的潜在核心基因，并揭示潜在的分子机制。通过对来自基因表达综合 (GEO) 的多个数据集的综合分析，鉴定出 414 个差异表达基因 (DEG)。将蛋白质-蛋白质相互作用（PPI）网络分析与基因集富集分析（GSEA）相结合，得到包括CCNA2、CCNB1、CCNB2、AURKA、CDK1、MAD2L1、BUB1B和RRM2在内的8个核心基因，以及4个核心通路，包括选择“细胞周期”、“卵母细胞减数分裂”、“p53途径”和“DNA复制”。在独立数据集中，核心基因显示出卓越的诊断价值和显着的预后相关性。而且，在癌症基因组图谱（TCGA）的泛癌数据中，核心基因也广泛异常表达。综上所述，本研究通过综合分析确定了MB的核心基因和通路，加深了对MB分子机制的认识，为MB的诊断和治疗提供了潜在的靶点和途径。