Author(s):
Bernardino, Rui Miguel Marques ; Leão, Ricardo ; Henrique, Rui ; Pinheiro, Luis Campos ; Kumar, Prashant ; Suravajhala, Prashanth ; Beck, Hans Christian ; Carvalho, Ana Sofia ; Matthiesen, Rune
Date: 2021
Persistent ID: http://hdl.handle.net/10362/130894
Origin: Repositório Institucional da UNL
Subject(s): Biomarkers; Extracellular vesicles (EVs); Prostate cancer; Proteomics; Urine extracellular vesicles (uEVs); Catalysis; Molecular Biology; Spectroscopy; Computer Science Applications; Physical and Theoretical Chemistry; Organic Chemistry; Inorganic Chemistry; SDG 3 - Good Health and Well-being
Description
Funding Information: Funding: R.M. is supported by Fundação para a Ciência e a Tecnologia (CEEC position, 2019–2025 investigator). This article is a result of the projects (iNOVA4Health—UIDB/04462/2020), supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work is also funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under the projects number PTDC/BTM-TEC/30087/2017 and PTDC/BTM-TEC/30088/2017. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly ex-plored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.
