alexa Strategy for SRM-based verification of biomarker candidates discovered by iTRAQ method in limited breast cancer tissue samples.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Theoretical and Computational Science

Author(s): Muraoka S, Kume H, Watanabe S, Adachi J, Kuwano M,

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Abstract Since LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications over the past decade, numerous studies have performed relative and/or absolute abundance determinations across large sets of proteins. In this study, we discovered prognostic biomarker candidates from limited breast cancer tissue samples using discovery-through-verification strategy combining iTRAQ method followed by selected reaction monitoring/multiple reaction monitoring analysis (SRM/MRM). We identified and quantified 5122 proteins with high confidence in 18 patient tissue samples (pooled high-risk (n=9) or low-risk (n=9)). A total of 2480 proteins (48.4\%) of them were annotated as membrane proteins, 16.1\% were plasma membrane and 6.6\% were extracellular space proteins by Gene Ontology analysis. Forty-nine proteins with >2-fold differences in two groups were chosen for further analysis and verified in 16 individual tissue samples (high-risk (n=9) or low-risk (n=7)) using SRM/MRM. Twenty-three proteins were differentially expressed among two groups of which MFAP4 and GP2 were further confirmed by Western blotting in 17 tissue samples (high-risk (n=9) or low-risk (n=8)) and Immunohistochemistry (IHC) in 24 tissue samples (high-risk (n=12) or low-risk (n=12)). These results indicate that the combination of iTRAQ and SRM/MRM proteomics will be a powerful tool for identification and verification of candidate protein biomarkers. This article was published in J Proteome Res and referenced in Journal of Theoretical and Computational Science

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