Protein　O-glycosylation　has　long　been　recognized　to　be　closely　associated　with　many　diseases,　particularly　with　tumor　proliferation,　invasion,　and　metastasis.　The　ability　to　efficiently　profile　the　variation　of　O-glycosylation　in　large-scale　clinical　samples　provides　an　important　approach　for　the　development　of　biomarkers　for　cancer　diagnosis　and　for　therapeutic　response　evaluation.　Therefore,　mass　spectrometry　(MS)-based　techniques　for　high　throughput,　in-depth　and　reliable　elucidation　of　protein　O-glycosylation　in　large　clinical　cohorts　are　in　high　demand.　However,　the　wide　existence　of　serine　and　threonine　residues　in　the　proteome　and　the　tens　of　mammalian　O-glycan　types　lead　to　extremely　large　searching　space　composed　of　millions　of　theoretical　combinations　of　peptides　and　O-glycans　for　intact　O-glycopeptide　database　searching.　As　a　result,　an　exceptionally　long　time　is　required　for　database　searching,　which　is　a　major　obstacle　in　O-glycoproteome　studies　of　large　clinical　cohorts.　More　importantly,　because　of　the　low　abundance　and　poor　ionization　of　intact　O-glycopeptides　and　the　stochastic　nature　of　data-dependent　MS2　acquisition,　substantially　elevated　missing　data　levels　are　inevitable　as　the　sample　number　increases,　which　undermines　the　quantitative　comparison　across　samples.　Therefore,　we　report　a　new　MS　data　processing　strategy　that　integrates　glycoform-specific　database　searching,　reference　library-based　MS1　feature　matching　and　MS2　identification　propagation　for　fast　identification,　in-depth,　and　reproducible　label-free　quantification　of　O-glycosylation　of　human　urinary　proteins.　This　strategy　increases　the　database　searching　speeds　by　up　to　20-fold　and　leads　to　a　30%-40%　enhanced　intact　O-glycopeptide　quantification　in　individual　samples　with　an　obviously　improved　reproducibility.　In　total,　we　identified　1300　intact　O-glycopeptides　in　36　healthy　human　urine　samples　with　a　30%-40%　reduction　in　the　amount　of　missing　data.　This　is　currently　the　largest　dataset　of　urinary　O-glycoproteome　and　demonstrates　the　application　potential　of　this　new　strategy　in　large-scale　clinical　investigations.