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Submitted on June 9, 2005
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205
Corresponding Author: pandey{at}jhmi.edu
Quantitative proteomics can be used as a screening tool for identification of differentially expressed proteins as potential biomarkers for cancers. Candidate biomarkers from such studies can be subsequently tested using other techniques for use in early detection of cancers. Here, we demonstrate the use of Stable Isotope Labeling with Amino acids in Cell culture (SILAC) method to compare the secreted proteins (secretome) from pancreatic cancer-derived cells with that from non-neoplastic pancreatic ductal cells. We identified 145 differentially secreted proteins (>1.5 fold-change) several of which were previously reported as either upregulated (e.g. cathepsin D, M-CSF and fibronectin receptor) or downregulated (e.g. profilin 1 and IGFBP-7) proteins in pancreatic cancer, confirming the validity of our approach. In addition, we identified several proteins that have previously not been correlated with pancreatic cancer including perlecan (HSPG2), CD9 antigen, fibronectin receptor (integrin beta 1) and a novel cytokine designated as predicted osteoblast protein (FAM3C). The differential expression of a subset of these novel proteins was validated by Western blot analysis. In addition, overexpression of several proteins previously not described to be elevated in human pancreatic cancer (CD9, perlecan, SDF4, ApoE and fibronectin receptor) was confirmed by immunohistochemical labeling using pancreatic cancer tissue microarrays suggesting that these could be further pursued as potential biomarkers. Lastly, the protein expression data from SILAC were compared to mRNA expression data obtained using gene expression microarrays for the two cell lines (Panc1 and HPDE), and a correlation coefficient (r) of 0.28 was obtained, confirming previously reported poor associations between RNA and protein expression studies.
Revised on October 6, 2005
Accepted on October 8, 2005
Biomarker discovery from pancreatic cancer secretome using a differential proteomics approach
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