Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer Pedraz Valdunciel, Carlos Giannoukakos, Stavros Panagiotis Fernández Hilario, Alberto Luis Hackenberg, Michael circRNAs Extracellular vesicles nCounter Lung cancer NSCLC Liquid biopsies 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 preamplification 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 L 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. 2022-11-21T13:40:10Z 2022-11-21T13:40:10Z 2022-09-24 journal article Pedraz-Valdunciel, C... [et al.]. Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer. Pharmaceutics 2022, 14, 2034. [https://doi.org/10.3390/pharmaceutics14102034] https://hdl.handle.net/10481/78070 10.3390/pharmaceutics14102034 eng info:eu-repo/grantAgreement/EC/H2020/765492 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI