Glycosylation is one of the most common and dynamic post-translational modification of cell surface and secreted proteins. Cancer cells display unique glycosylation patterns that decisively contribute to drive oncogenic behavior, including disease progression and dissemination. Moreover, alterations in glycosylation are often responsible for the creation of protein signatures holding significant biomarker value and potential for targeted therapeutics. Accordingly, many analytical protocols have been outlined for the identification of abnormally glycosylated proteins by mass spectrometry. Nevertheless, very few studies undergo a comprehensive mining of the generated data. Herein, we build on bladder cancer O-glycoproteomics datasets resulting from a hyphenated technique comprising enrichment by Vicia villosa agglutinin (VVA) lectin and nanoLC-ESI-MS/MS to propose an in silico step-by-step tutorial (Panther, UniProtKB, NetOGlyc, NetNGlyc, Oncomine, Cytoscape) for biomarker discovery in cancer. We envisage that this approach may be generalized to other mass spectrometry-based analytical approaches, including N-glycoproteomics studies, and different types of cancers.
Significance: The glycoproteome is an important source of cancer biomarkers holding tremendous potential for targeted therapeutics. We now present an in silico roadmap for comprehensive interpretation of big data generated by mass spectrometry-based glycoproteomics envisaging the identification of clinically relevant glycobiomarkers.
Keywords: Bioinformatics; Biomarker discovery; Cancer biomarkers; Glycobiology; Glycoproteomics; Glycosylation.
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