Extracellular vesicles (EVs) are lipid bound vesicles secreted by cells into the extracellular space to enable intercellular communication. Extracellular vesicle surface proteins serve as the fundamental signaling gateways, determining the function of extracellular vesicles to communicate and interact with their environment. The surface proteins of EVs and their post-translational modifications are highly valuable sources of potential disease biomarkers and therapeutic targets, making the exploration of EV surface proteome (surfaceome) in circulating biofluids highly attractive. However, due to the unique features of surface proteins (e.g. hydrophobicity and glycosylation), highly selective and in-depth profiling of EV surfaceome still faces substantial technical challenges. Herein, we developed a strategy for comprehensively profiling the EV surfaceome based on the chemical affinity isolation of EVs and in situ labeling of EV surface glycoproteins for their downstream enrichment and identification. The developed method demonstrated significant efficacy in clinical samples at multiple centers with quantification of over 4,000 unique EV surface peptides from only 1 mL of urine. By characterizing the EV surfaceome from urine samples collected from 172 individuals, including 111 prostate cancer patients and 61 non-cancer controls, a comprehensive dataset of EV surfaceome comprising 1,121 proteins was achieved, representing the largest urine-derived EV surface protein dataset to date. Notably, the changes in the EV surfaceome patterns uncovered alterations of surface protein signatures during prostate cancer development and progression, validated using urine samples from an independent cohort of 196 additional individuals, including 144 prostate cancer patients and 52 non-cancer controls. This study identified cathepsin L and integrin alpha 3 as characteristic proteins with potential as non-invasive biomarkers for the early diagnosis of prostate cancer, while fibrinogen beta chain and tetraspanin-1 emerged as key proteins for prostate cancer grading. The clinical performance underscores the innovation and practicality of this approach, establishing it as a robust tool with potential for widespread clinical application.