Differential Protein Expressions in Renal Cell Carcinoma: New Biomarker Discovery by Mass Spectrometry

• Published in: Journal of proteome research 2009-08, Vol.8 (8), p.3797-3807
• Main Authors: Siu, K. W. Michael, DeSouza, Leroi V., Scorilas, Andreas, Romaschin, Alexander D., Honey, R. John, Stewart, Robert, Pace, Kenneth, Youssef, Youssef, Chow, Tsz-fung F., Yousef, George M.
• Format: Article
• Language: English
• Subjects: Biomarkers, Tumor - biosynthesis; Biomarkers, Tumor - genetics; Carcinoma, Renal Cell - genetics; Carcinoma, Renal Cell - metabolism; Chromatography, Liquid - methods; Gene Expression Profiling; Humans; Immunohistochemistry; Isotope Labeling; Kidney Neoplasms - genetics; Kidney Neoplasms - metabolism; Models, Genetic; Neoplasm Proteins - biosynthesis; Neoplasm Proteins - genetics; Proteomics - methods; Reproducibility of Results; Tandem Mass Spectrometry - methods
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/pr800389e

Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney. Unfortunately, there are currently no biomarkers for the diagnosis of RCC. In addition to early detection, biomarkers have a potential use for prognosis, for monitoring recurrence after treatment, and as predictive markers for treatment efficiency. In this study, we identified proteins that are dysregulated in RCC, utilizing a quantitative mass spectrometry analysis. We compared the protein expression of kidney cancer tissues to their normal counterparts from the same patient using LC−MS/MS. iTRAQ labeling permitted simultaneous quantitative analysis of four samples (cancer, normal, and two controls) by separately tagging the peptides in these samples with four cleavable mass-tags (114, 115, 116, and 117 Da). The samples were then pooled, and the tagged peptides resolved first by strong cation exchange chromatography and then by nanobore reverse phase chromatography coupled online to nanoelectrospray MS/MS. We identified a total of 937 proteins in two runs. There was a statistically significant positive correlation of the proteins identified in both runs (r p = 0.695, p < 0.001). Using a cutoff value of 0.67 fold for underexpression and 1.5 fold for overexpression, we identified 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to normal tissues. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. We performed an in silico validation of our results using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis.