PURPOSE: The purpose of this research was to perform a preliminary assessment of protein patterns in primary brain tumors using a direct-tissue mass spectrometric technique to profile and map biomolecules. EXPERIMENTAL DESIGN: We examined 20 prospectively collected, snap-frozen normal brain and brain tumor specimens using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS), and compared peptide and protein expression in primary brain tumor and nontumor brain tissues. RESULTS: MS can be used to identify protein expression patterns in human brain tissue and tumor specimens. The mass spectral patterns can reliably identify glial neoplasms of similar histological grade and differentiate them from tumors of different histological grades as well as from nontumor brain tissues. Initial bioinformatics cluster analysis algorithms classified tumor and nontumor tissues into similar groups comparable with their histological grade. CONCLUSIONS: We describe a novel tool for the analysis of protein expression patterns in human glial neoplasms. Initial results demonstrate that MALDI-MS technology can significantly aid in the process of unraveling and understanding the molecular complexities of gliomas. MALDI-MS accurately and reliably identified normal and neoplastic tissues, and could be used to discriminate between tumors of increasing grades.
PURPOSE: The purpose of this research was to perform a preliminary assessment of protein patterns in primary brain tumors using a direct-tissue mass spectrometric technique to profile and map biomolecules. EXPERIMENTAL DESIGN: We examined 20 prospectively collected, snap-frozen normal brain and brain tumor specimens using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS), and compared peptide and protein expression in primary brain tumor and nontumor brain tissues. RESULTS: MS can be used to identify protein expression patterns in human brain tissue and tumor specimens. The mass spectral patterns can reliably identify glial neoplasms of similar histological grade and differentiate them from tumors of different histological grades as well as from nontumor brain tissues. Initial bioinformatics cluster analysis algorithms classified tumor and nontumor tissues into similar groups comparable with their histological grade. CONCLUSIONS: We describe a novel tool for the analysis of protein expression patterns in humanglial neoplasms. Initial results demonstrate that MALDI-MS technology can significantly aid in the process of unraveling and understanding the molecular complexities of gliomas. MALDI-MS accurately and reliably identified normal and neoplastic tissues, and could be used to discriminate between tumors of increasing grades.
Authors: Ovidiu C Andronesi; Konstantinos D Blekas; Dionyssios Mintzopoulos; Loukas Astrakas; Peter M Black; A Aria Tzika Journal: Int J Oncol Date: 2008-11 Impact factor: 5.650
Authors: Stefan K Maier; Hannes Hahne; Amin Moghaddas Gholami; Benjamin Balluff; Stephan Meding; Cédrik Schoene; Axel K Walch; Bernhard Kuster Journal: Mol Cell Proteomics Date: 2013-06-19 Impact factor: 5.911
Authors: Peter B Harrington; Claudine Laurent; Douglas F Levinson; Pat Levitt; Sanford P Markey Journal: Anal Chim Acta Date: 2007-08-06 Impact factor: 6.558
Authors: Maxime S Heroux; Marla A Chesnik; Brian D Halligan; Mona Al-Gizawiy; Jennifer M Connelly; Wade M Mueller; Scott D Rand; Elizabeth J Cochran; Peter S LaViolette; Mark G Malkin; Kathleen M Schmainda; Shama P Mirza Journal: Physiol Genomics Date: 2014-05-06 Impact factor: 3.107