TY - JOUR
T1 - Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer
AU - Pedraz-Valdunciel, Carlos
AU - Giannoukakos, Stavros
AU - Giménez-Capitán, Ana
AU - Fortunato, Diogo
AU - Filipska, Martyna
AU - Bertran-Alamillo, Jordi
AU - Bracht, Jillian W.P.
AU - Drozdowskyj, Ana
AU - Valarezo, Joselyn
AU - Zarovni, Natasa
AU - Fernández-Hilario, Alberto
AU - Hackenberg, Michael
AU - Aguilar-Hernández, Andrés
AU - Molina-Vila, Miguel Ángel
AU - Rosell, Rafael
N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement ELBA No 765492. Publisher Copyright: © 2022 by the authors.
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - NSCLC
KW - circRNAs
KW - extracellular vesicles
KW - liquid biopsies
KW - lung cancer
KW - nCounter
UR - http://www.scopus.com/inward/record.url?scp=85140835400&partnerID=8YFLogxK
U2 - https://doi.org/10.3390/pharmaceutics14102034
DO - https://doi.org/10.3390/pharmaceutics14102034
M3 - Article
C2 - 36297470
SN - 1999-4923
VL - 14
JO - Pharmaceutics
JF - Pharmaceutics
IS - 10
M1 - 2034
ER -