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Alpha-2-macroglobulin as a novel diagnostic biomarker for human bladder cancer in urinary extracellular vesicles

Extracellular vesicles (EVs) derived from urine are promising tools for the diagnosis of urogenital cancers. Urinary EVs (uEVs) are considered potential biomarkers for bladder cancer (BC) because urine is in direct contact with the BC tumor microenvironment and thus reflects the current state of the...

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Detalles Bibliográficos
Autores principales: Lee, Jisu, Park, Hyun Sik, Han, Seung Ro, Kang, Yun Hee, Mun, Ji Young, Shin, Dong Wook, Oh, Hyun-Woo, Cho, Yoon-Kyoung, Lee, Myung-Shin, Park, Jinsung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9513419/
https://www.ncbi.nlm.nih.gov/pubmed/36176383
http://dx.doi.org/10.3389/fonc.2022.976407
Descripción
Sumario:Extracellular vesicles (EVs) derived from urine are promising tools for the diagnosis of urogenital cancers. Urinary EVs (uEVs) are considered potential biomarkers for bladder cancer (BC) because urine is in direct contact with the BC tumor microenvironment and thus reflects the current state of the disease. However, challenges associated with the effective isolation and analysis of uEVs complicate the clinical detection of uEV-associated protein biomarkers. Herein, we identified uEV-derived alpha-2-macroglobulin (a2M) as a novel diagnostic biomarker for BC through comparative analysis of uEVs obtained from patients with BC pre- and post-operation using an antibody array. Furthermore, enzyme-linked immunosorbent assay of uEVs isolated from patients with BC (n=60) and non-cancer control subjects (n=23) validated the significant upregulation of a2M expression in patient uEVs (p<0.0001). There was no significant difference in whole urine a2M levels between patients with BC and controls (p=0.317). We observed that compared to classical differential centrifugation, ExoDisc, a centrifugal microfluidic tangential flow filtration device, was a significantly more effective separation method for uEV protein analysis. We expect that our approach for EV analysis will provide an efficient route for the identification of clinically meaningful uEV-based biomarkers for cancer diagnosis.