Mehrdad Hashemi - Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
Mehdi Pooladi - Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
Solmaz Khaghani Razi Abad - Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran
Abolfazl Movafagh - Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Maliheh Entezari - Department of Genetics, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran

Background: Gliomas are the most frequently observed primary brain tumors. These tumors comprise a variety of different histological tumor types and malignancy grades. Oligodendrogliomas typically contain a rich network of branching capillaries. Approximately ۵۰%-۸۰% of oligodendrogliomas demonstrate a combined loss of chromosomes ۱p and ۱۹q. Oligodendrogliomas differ from neurocytomas in that they show a diffusely infiltrating pattern of spread that precludes surgical cure.Methods: We evaluated extracted proteins from tumors and normal brain tissues for protein purity by the Bradford test and spectrophotometry. We separated proteins by two-dimensional gel electrophoresis. The spots were analyzed and compared using statistical data and MALDI-TOF/TOF. Protein clustering analyses were performed on the list of proteins deemed significantly altered in oligodendroglioma tumor tissues.Results: On each analytical two-dimensional gel, we observed an average of ۱۳۲۸ spots. A total of ۱۵۷ exhibited up-regulation of expression levels, whereas the remaining ۲۷۶ spots had decreased expression in astrocytoma tumors relative to normal tissue. The results demonstrated that functional clustering and principal component analysis had considerable merit in aiding the interpretation of proteomic data.Conclusion: Clustering methodology is a powerful data mining approach for initial exploration of proteomic data. The clustering results depend on parameters such as data preprocessing, between-profile similarity measurement and the dendrogram construction procedure.