Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer
Pedraz-Valdunciel, Carlos (Institut Germans Trias i Pujol)
Giannoukakos, Stavros (Universidad de Granada)
Giménez-Capitán, A. (Dexeus University Hospital)
Fortunato, D. (Exosomics SpA)
Filipska, Martyna (Institut Hospital del Mar d'Investigacions Mèdiques)
Bertran-Alamillo, Jordi (Dexeus University Hospital)
Bracht, Jillian (Universiteit van Amsterdam)
Drozdowskyj, Ana (Dexeus University Institute)
Valarezo, J. (Dexeus University Hospital)
Zarovni, N. (Exosomics SpA)
Fernandez Hilario, Alberto (University of Granada)
Hackenberg, Michael (Universidad de Granada)
Aguilar-Hernández, Andrés (Dexeus University Institute)
Molina Vila, Miguel Ángel (Dexeus University Hospital)
Rosell, Rafael (Institut Germans Trias i Pujol. Hospital Universitari Germans Trias i Pujol)
Universitat Autònoma de Barcelona

Fecha:	2022
Resumen:	Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 μL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0. 86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures.
Ayudas:	European Commission 765492
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	CircRNAs ; Extracellular vesicles ; Liquid biopsies ; Lung cancer ; Ncounter ; NSCLC
Publicado en:	Pharmaceutics, Vol. 14 Núm. 10 (october 2022) , p. 2034, ISSN 1999-4923

DOI: 10.3390/pharmaceutics14102034
PMID: 36297470

18 p, 4.4 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Institut d'Investigació en Ciencies de la Salut Germans Trias i Pujol (IGTP)
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