Introduction: One of the major challenges in cancer treatment is the lack of specific and accurate treatment in cancer. Data analysis can help to understand the underlying molecular mechanism that leads to better treatment. Increasing availability and reliability of DNA microarray data leads to increase the use of these data in a variety of cancers. This study aimed at applying and evaluating microarray data analyzing, identification of important pathways and gene network for medulloblastoma patients to improve treatment approaches especially target therapy. Methods: In the current study, Microarray gene expression data (GSE50161) were extracted from Geo datasets and then analyzed by the affylmGUI package to predict and investigate upregulated and downregulated genes in medulloblastoma. Then, the important pathways were determined by using software and gene enrichment analyses. Pathways visualization and network analyses were performed by Cytoscape. Results: A total number of 249 differentially expressed genes (DEGs) were identified in medulloblastoma compared to normal samples. Cell cycle, p53, and FoxO signaling pathways were indicated in medulloblastoma, and CDK1, CCNB1, CDK2, and WEE1 were identified as some of the important genes in the medulloblastoma. Conclusion: Identification of critical and specific pathway in any disease, in our case medulloblastoma, can lead us to better clinical management and accurate treatment and target therapy.
Keywords: Medulloblastoma; computational biology; differentially expressed genes; KEGG pathways; protein.
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