Abstract
Because of the high complexity of human plasma, it is normally hard to detect secreted proteins to discover the useful cancer biomarkers. To discover candidate disease markers in the human plasma, therefore, is challenging in proteomics. We used pooled plasma sample of normal and cancer patients to statistically profile peptide patterns from the plasma proteins by mass spectrometry (MS). From the peptide pattern profiling with quantitative MS analysis, we discovered the group of peptides from glycoproteins, each of which showed quite different quantity from other peptides belonging to the same glycoproteins. For validating the targeted peptides from different plasma samples, multiple reaction monitoring from linear ion trap with Fourier Transform MS was tried and also run by principal component analysis. From this method, we clearly classified the candidate peptides from normal and cancer group for cancer biomarker discovery.
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