High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis
Hao Zhang,
Gui-Yuan Zhang,
Wei-Chao Su,
Ya-Ting Chen,
Yu-Feng Liu,
Dong Wei,
Yan-Xi Zhang,
Qiu-Yi Tang,
Yu-Xiang Liu,
Shi-Zhi Wang,
Wen-Chao Li,
Anke Wesselius,
Maurice P. Zeegers,
Zi-Yu Zhang,
Yan-Hong Gu,
W. Andy Tao,
Evan Yi-Wen Yu
Affiliations
Hao Zhang
State Key Laboratory of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, Southeast University, Nanjing 210096, China
Gui-Yuan Zhang
State Key Laboratory of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, Southeast University, Nanjing 210096, China
Wei-Chao Su
Department of Colorectal Tumor Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361003, China
Ya-Ting Chen
Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
Yu-Feng Liu
State Key Laboratory of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, Southeast University, Nanjing 210096, China
Dong Wei
State Key Laboratory of Bioelectronics, National Demonstration Center for Experimental Biomedical Engineering Education, Southeast University, Nanjing 210096, China
Yan-Xi Zhang
Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
Qiu-Yi Tang
Medical School of Southeast University, Nanjing 210009, China
Yu-Xiang Liu
Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
Shi-Zhi Wang
Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
Wen-Chao Li
Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing 210009, China
Anke Wesselius
Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, 6229ER Maastricht, The Netherlands
Maurice P. Zeegers
Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, 6229ER Maastricht, The Netherlands
Zi-Yu Zhang
Department of Pathology, Jiangxi Maternal & Child Health Hospital, Nanchang 330006, China
Yan-Hong Gu
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing 210029, China
W. Andy Tao
Departments of Chemistry and Biochemistry, Purdue University, West Lafayette, IN 47907, USA
Evan Yi-Wen Yu
Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, Department of Epidemiology & Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China
Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. 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 three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.
