Document Type : Research Articles
Authors
1
Department of Cellular-Molecular Biology, Faculty of Biological Sciences and Technologies, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2
Department of Cellular-Molecular Biology, Faculty of Biological Sciences and Technologies, Shahid Beheshti University. Tehran, Iran.
3
Department of Dental Sciences,, Shahid Beheshti University, Tehran, Iran.
4
Department of Hematology and Medical Oncology, Shahid Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
5
Department of Biology, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
6
Cancer Research Center, Shohadae Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
7
Department of Pathology, Shohadae Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
8
Department of Mycobacteriology and Pulmonary Research, Microbiology Research Center (MRC), Pasteur Institute of Iran, Tehran,
9
Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Abstract
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
Main Subjects
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