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Updated: Jan 16, 2026

Author Spotlight: Advancing EVtrap for High-Throughput Proteomics in Disease Biomarker Discovery
Published on: October 27, 2023
Differential Proteomics of Large Extracellular Vesicles in Ovarian Cancer
Kazuhiro Suzuki1, Yusuke Yamamoto2, Masami Kitagawa1
1Department of Obstetrics and Gynecology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Abstract:
Diverse extracellular vesicles (EVs) are present in all body fluids; however, knowledge of large EVs (lEVs) remains limited. Molecular EV profiles vary depending on EV size and the physiological circulatory system, even within the same patient. In this study, we aimed to characterize the proteomic profile of IEVs in ovarian cancer patients and identify lEV-protein biomarkers. We collected tissue, serum, and ascites from patients with high-grade serous ovarian cancer and concurrently separated small EVs (sEVs) and lEVs through sequential multistep centrifugation. Proteomic analysis of tissues and EVs revealed distinct EV profiles in serum and ascites, identifying 11 lEV-specific proteins in serum and 14 in ascites that were absent in sEV. Of these, seven serum-specific and 10 ascites-specific proteins were further analyzed using transcriptomic databases, revealing candidate diagnostic and prognostic lEV-protein biomarkers. Our findings underscore the importance of size-based EV separation, as particle size influences biosynthetic mechanisms, in identifying lEV-specific proteins with potential diagnostic and prognostic values. SUMMARY: This study underscores the importance of distinguishing extracellular vesicle (EV) subtypes and considering body fluid specificity in biomarker discovery. By isolating EVs based on size and stepwise separation and analyzing their proteomic profiles in ovarian cancer, we identified potential large EV (lEV)-specific biomarkers that reflect disease pathology. These findings provide a foundation for lEV-protein-based liquid biopsy approaches that could enhance the accuracy of early detection and patient stratification. Further validation in clinical settings may pave the way for more precise and personalized ovarian cancer diagnostics.
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