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Lab Invest. 2007 Aug;87(8):755-66. Epub 2007 Jun 11.

Global proteomic analysis distinguishes biologic differences in head and neck squamous carcinoma.

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Department of Pathology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10461, USA.


The goal of this study was to establish a method for detecting biologically significant differences in protein expression of head and neck squamous cell carcinoma (HNSCC) obtained from the same samples utilized in gene expression analyses. Proteins from two head and neck tumor cell lines, SCC-25 and FaDu, were isolated from the denatured protein solution remaining from the TRIzol extraction procedure used for isolation of total RNA for microarray analysis. Peptides resulting from chemical and enzymatic digestion of the proteins were first separated by strong cation-exchange chromatography, followed by liquid chromatography-mass spectrometry (LC-MS) analysis on a QqTOF mass spectrometer. Stable isotope-labeled synthetic peptides were added to each ion-exchange fraction as internal standards, for reversed-phase HPLC retention time alignment. Protein extraction and digestion were repeated three times for each cell line and each extract was analyzed three times by LC-MS. To discriminate between technical vs biological variation, the ion-exchange fraction, retention time, normalized mass and signal intensity of these nine data sets were constructed into numerical arrays for statistical analysis. Of the approximately 50,000 signals, 90 peptide ions were found to discriminate the two cell lines with high stringency. Of those, six peptides were derived from vimentin and four peptides were derived from annexin II; both expressed more in SCC-25. Follow-up analysis of some of these signals by LC-MS/MS and RNA expression profiling revealed both concordance and discordance of RNA and protein expression. This study demonstrates that this procedure is highly reliable for identifying peptides that distinguish biological variability among samples, indicating that this method can be applied to study clinical samples, to identify potential prognostic biomarkers for HNSCC.

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