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dc.contributor.authorPedraz Valdunciel, Carlos
dc.contributor.authorGiannoukakos, Stavros Panagiotis 
dc.contributor.authorM Potie, Nicolas Thierry 
dc.contributor.authorHackenberg, Michael 
dc.contributor.authorFernández Hilario, Alberto Luis 
dc.date.accessioned2022-03-09T08:38:14Z
dc.date.available2022-03-09T08:38:14Z
dc.date.issued2022-01-21
dc.identifier.citationPedraz-Valdunciel, C... [et al.] (2022), Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens. Mol Oncol. [https://doi.org/10.1002/1878-0261.13182]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/73242
dc.descriptionWe would like to thank Stephanie Davis for her language editing assistance. The investigators also wish to thank the patients for kindly agreeing to donate samples to this study. We thank all the physicians who collaborated by providing clinical information. Graphical Abstract, Figs 1A, 8A and Fig. S1 were created with Biorender.com. This project has received funding from a European Union's Horizon 2020 research and innovation program under the Marie SklodowskaCurie grant agreement ELBA No 765492.es_ES
dc.description.abstractAlthough many studies highlight the implication of circular RNAs (circRNAs) in carcinogenesis and tumor progression, their potential as cancer biomarkers has not yet been fully explored in the clinic due to the limitations of current quantification methods. Here, we report the use of the nCounter platform as a valid technology for the analysis of circRNA expression patterns in non-small cell lung cancer (NSCLC) specimens. Under this context, our custom-made circRNA panel was able to detect circRNA expression both in NSCLC cells and formalin-fixed paraffinembedded (FFPE) tissues. CircFUT8 was overexpressed in NSCLC, contrasting with circEPB41L2, circBNC2, and circSOX13 downregulation even at the early stages of the disease. Machine learning (ML) approaches from different paradigms allowed discrimination of NSCLC from nontumor controls (NTCs) with an 8-circRNA signature. An additional 4-circRNA signature was able to classify early-stage NSCLC samples from NTC, reaching a maximum area under the ROC curve (AUC) of 0.981. Our results not only present two circRNA signatures with diagnosis potential but also introduce nCounter processing following ML as a feasible protocol for the study and development of circRNA signatures for NSCLC.es_ES
dc.description.sponsorshipEuropean Commission 765492es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sonses_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectBiomarkerses_ES
dc.subjectCancer es_ES
dc.subjectCircRNAes_ES
dc.subjectDiagnosis es_ES
dc.subjectnCounteres_ES
dc.subjectNSCLCes_ES
dc.titleDigital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimenses_ES
dc.typejournal articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/765492es_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.1002/1878-0261.13182
dc.type.hasVersionVoRes_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España