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Bladder Cancer (BC) presents one of the highest recurrence rates amongst solid tumours and constitutes the second deadliest disease of the genitourinary track. Non-invasive identification of patients facing disease recurrence and/or progression remains one of the most critical and challenging aspects in disease management. To contribute to this goal, we demonstrate the potential of glycan-affinity glycoproteomics nanoplatforms for urinary biomarkers discovery in bladder cancer. Briefly, magnetic nanoprobes (MNP) coated with three broad-spectrum lectins, namely Concanavalin A (ConA; MNP@ConA), Wheat Germ Agglutinin (WGA; MNP@WGA), and Sambucus nigra (SNA; MNP@SNA), were used to selectively capture glycoproteins from the urine of low-grade and high-grade non-muscle invasive as well as muscle-invasive BC patients. Proteins were identified by nano-LC MALDI-TOF/TOF and data was curated using bioinformatics tools (UniProt, NetOGlyc, NetNGlyc, ClueGO app for Cytoscape and Oncomine) to highlight clinically relevant species. Accordingly, 63 glycoproteins were exclusively identified in cancer samples compared with healthy controls matching in age and gender. Specific glycoprotein sets exclusively found in low-grade non-muscle invasive bladder tumours may aid early diagnosis, while those only found in high-grade non-invasive and muscle-invasive tumours hold potential for accessing progression. Amongst these proteins is bladder cancer stem-cell marker CD44, which has been associated with poor prognosis. Orthogonal validation studies by slot-blotting demonstrated an elevation in urine CD44 levels of high-grade patients, which became more pronounced upon muscle-invasion, in mimicry of the primary tumour. These observations demonstrate the potential of MNP@lectins for identification of clinically relevant glycoproteomics signatures in bladder cancer. Future clinical validation in a larger and well characterized patient subset is required envisaging clinical translation of the results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]