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Proteomic expression analysis and comparison of protein and mRNA expression profiles in human malignant gliomas
Author(s) -
Persson Oscar,
Brynnel Ulrika,
Levander Fredrik,
Widegren Bengt,
Salford Leif G.,
Krogh Morten
Publication year - 2009
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.200800086
Subject(s) - messenger rna , protein expression , glioma , expression (computer science) , biology , cancer research , microbiology and biotechnology , gene , genetics , computer science , programming language
Gliomas are highly heterogeneous and therapy resistant tumors with a poor prognosis. Novel experimental therapeutic approaches have shown some promising results, but often target specific molecular mechanisms or antigens, and careful characterization of the molecular subgroup of the tumors will therefore likely be important. Thorough investigations of gene and protein alterations are also important to better understand the tumorigenic mechanisms. We have undertaken a proteomic approach, using 2‐D DIGE and LC‐MS/MS protein identification, to investigate 38 human gliomas and normal brains. We show that the proteome profile can discriminate between normal brain and tumors, and between tumors of varying grade by a supervised classifier. Furthermore, an analysis of the identified proteins shows an enrichment of proteins associated to pathways known to be central in gliomas, such as MEK/Erk signaling and actin cytoskeleton. It also shows a shift between different glial fibrillary acidic protein (GFAP) representatives in different grades. In a previous study the gene expression profile was characterized in an almost identical set of tumors, which enabled a paired analysis of the gene and protein expression profiles. We show that there is often a weak correlation between the mRNA and protein level. This, together with the ability of proteomics to identify PTMs, emphasizes the benefit of characterization on a protein level.