Cluster and Principal Component Analysis of Human Glioblastoma Multiforme (GBM) Tumor Proteome

Background: glioblastoma multiforme (gbm) or grade iv astrocytoma is the most common and lethal adult malignant brain tumor. several of the molecular alterations detected in gliomas may have diagnostic and/or prognostic implications. proteomics has been widely applied in various areas of science, ranging from the deciphering of molecular pathogen nests of discuses.methods: in this study proteins were extracted from the tumor and normal brain tissues and then the protein purity was evaluated by bradford test and spectrophotometry. in this study, proteins were separated by 2-dimensional gel (2dg) electrophoresis method and the spots were then analyzed and compared using statistical data and specific software. protein clustering analysis was performed on the list of proteins deemed significantly altered in glioblastoma tumors (t-test and one-way anova; p < 0.05).results: the 2d gel showed totally 876 spots. we reported, 172 spots were exhibited differently in expression level (fold > 2) for glioblastoma. on each analytical 2d gel, an average of 876 spots was observed. in this study, 188 spots exhibited up regulation of expression level, whereas the remaining 232 spots were decreased in glioblastoma tumor relative to normal tissue. results demonstrate that functional clustering (up and down regulated) and principal component analysis (pca) has considerable merits in aiding the interpretation of proteomic data.conclusion: 2d gel electrophoresis is the core of proteomics which permitted the separation of thousands of proteins. high resolution 2de can resolve up to 5,000 proteins simultaneously. using cluster analysis, we can also form groups of related variables, similar to what is practiced in factor analysis.