Proteomic Profiling of Urinary Large Extracellular Vesicles for the Diagnosis of Prostate Cancer

Distinguishing between prostate cancer (PCa) and prostatitis poses a challenge in clinical settings, often resulting in overdiagnosis and treatment. Large extracellular vesicles (lEVs) extracted from urine samples of patients may contain information about prostate pathology and are, therefore, potential biopsies for early PCa detection. This research endeavors to create a urinary lEV-centric proteomic approach for early PCa identification. An integrated workflow utilizing functionalized magnetic beads, centrifugation, and filter membranes optimizes and expedites the analysis of urinary lEVs. To validate the stability and significance of lEV proteomic markers, the study employed multiple reaction monitoring (MRM) to assess the identified differentially expressed proteins in lEVs (lEV-DEPs) within a validation group for precise quantification. Furthermore, cell-line experiments were conducted to compare the proteomic profiles of lEVs, small EVs (sEVs), and cell membranes (CM). In total, 3549 urinary lEV proteins were captured, revealing 17 lEV-DEPs, with 12 confirmed through MRM. Notably, this isolation method reduced contaminants compared to centrifugation, facilitating the extraction of relatively large EVs. Pathway enrichment analysis of lEV-DEPs underscored their role in transitioning from inflammation to cancer, with specific post-translational modifications (PTMs) influencing PCa progression. A panel integrating four potential lEV-DEPs and prostate-specific antigen (PSA) exhibited a distinguished accuracy of 0.875, emphasizing the importance of lEV-DEPs over PSA in distinguishing PCa from prostatitis. Additionally, lEVs demonstrated significant protein content and pathway similarity with CM compared to sEVs. In conclusion, this investigation establishes a comprehensive protocol for isolating and characterizing urinary lEV proteomics, showcasing its efficacy in discriminating PCa from prostatitis. These results enhance diagnostic precision and offer fresh insights into the biological mechanisms underlying PCa, paving the way for improved diagnostic and therapeutic approaches.

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Publication Details

Journal
Analytical Chemistry
Published
2025-07-16
DOI
https://doi.org/10.1021/acs.analchem.5c01320
Citations
1
Primary Topic
Extracellular vesicles in disease
Type
article
Field-Weighted Citation Impact
0.81

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Proteomic Profiling of Urinary Large Extracellular Vesicles for the Diagnosis of Prostate Cancer

Lu-Hua Chen, Yuxia Luan, Anke Wesselius, Yanhong Gu et al.
1 citations
Analytical Chemistry
Extracellular vesicles in disease
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article

Proteomic Profiling of Urinary Large Extracellular Vesicles for the Diagnosis of Prostate Cancer

Lu-Hua Chen, Yuxia Luan, Anke Wesselius, Yanhong Gu, Saisai Chen, Yuxuan Wu, Bing Han, Guiyuan Zhang, Haibin Hao, Jingyao Li, Qiu‐Yi Tang, Huiying Ren, Maurice Petrus Zeegers, Yuping Fan, Hao Zhang, Yuting Zhang, Menghan Li, Wei Liu, Le Ma, Yanxi Zhang, Wei-Chao Su, Jiang Xia, Ya‐Ting Chen, Qirong Qin, Ying Lv, Evan Yi-Wen Yu
article en
1 citations

Abstract

Distinguishing between prostate cancer (PCa) and prostatitis poses a challenge in clinical settings, often resulting in overdiagnosis and treatment. Large extracellular vesicles (lEVs) extracted from urine samples of patients may contain information about prostate pathology and are, therefore, potential biopsies for early PCa detection. This research endeavors to create a urinary lEV-centric proteomic approach for early PCa identification. An integrated workflow utilizing functionalized magnetic beads, centrifugation, and filter membranes optimizes and expedites the analysis of urinary lEVs. To validate the stability and significance of lEV proteomic markers, the study employed multiple reaction monitoring (MRM) to assess the identified differentially expressed proteins in lEVs (lEV-DEPs) within a validation group for precise quantification. Furthermore, cell-line experiments were conducted to compare the proteomic profiles of lEVs, small EVs (sEVs), and cell membranes (CM). In total, 3549 urinary lEV proteins were captured, revealing 17 lEV-DEPs, with 12 confirmed through MRM. Notably, this isolation method reduced contaminants compared to centrifugation, facilitating the extraction of relatively large EVs. Pathway enrichment analysis of lEV-DEPs underscored their role in transitioning from inflammation to cancer, with specific post-translational modifications (PTMs) influencing PCa progression. A panel integrating four potential lEV-DEPs and prostate-specific antigen (PSA) exhibited a distinguished accuracy of 0.875, emphasizing the importance of lEV-DEPs over PSA in distinguishing PCa from prostatitis. Additionally, lEVs demonstrated significant protein content and pathway similarity with CM compared to sEVs. In conclusion, this investigation establishes a comprehensive protocol for isolating and characterizing urinary lEV proteomics, showcasing its efficacy in discriminating PCa from prostatitis. These results enhance diagnostic precision and offer fresh insights into the biological mechanisms underlying PCa, paving the way for improved diagnostic and therapeutic approaches.

Analytical ChemistryVol. 97(32)
Wannan Medical College (CN), Harbin Medical University (CN), Chinese University of Hong Kong (HK), Shanghai Jiao Tong University (CN), Nanjing General Hospital of Nanjing Military Command (CN), XinHua Hospital (CN), Maastricht University (NL), Zhongda Hospital Southeast University (CN), Digital Science (United States) (US), Ministry of Education (KR), Evive Biotech (China) (CN), Faculty of Public Health (GB), Taiwan Centers for Disease Control (TW), Jiangsu Province Hospital (CN), First Affiliated Hospital of Harbin Medical University (CN), Shanghai Clinical Research Center (CN), Tianjin haihe hospital (CN), State Key Laboratory of Digital Medical Engineering (CN), Nanjing Medical University (CN), Nanjing University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province, Southeast University, Anhui Provincial Department of Education, Fundamental Research Funds for the Central Universities
Good health and well-being
Openalex Percentile: Top 26%
Extracellular vesicles in disease
0.81
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