Proteomic Profile of Urinary Extracellular Vesicles Unravelled Biomarkers for Prostate Cancer Recognition

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer, which was aimed for identifying novel biomarkers for prostate cancer diagnosis in this study. The focus of this study was to develop a robust data independent acquisition (DIA) using mass spectrometry to analyse urinary EV proteomics for prostate cancer and prostate inflammation screening. We combined three library-based analysis (direct-DIA, GPF-DIA, and fractionated DDA) to improve the stability and comprehensiveness of biomarkers. By applying this innovative DIA strategy in conjunction with stable automatic EVs extraction technology, we assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which 3 (i.e., SERPINA3, LRG1, SCGB3A1) were shown to be consistently up regulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed interactions. Moreover, the potential mechanistic pathways of significantly different EV proteins, were enriched in metabolic, immune, and inflammatory activities. These results showed consistent in an independent cohort consist of 20 participants. Based on random forest algorithm, we found that SERPINA3, LRG1, SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI) and prostate-specific antigen (PSA). In summary, the current study revealed the EV proteomic landscape and biomarkers for prostate cancer, which has shown to provide promising insights of urine EV proteome in clinical implication.