dc.contributor.author | Pedraz Valdunciel, Carlos | |
dc.contributor.author | Giannoukakos, Stavros Panagiotis | |
dc.contributor.author | M Potie, Nicolas Thierry | |
dc.contributor.author | Hackenberg, Michael | |
dc.contributor.author | Fernández Hilario, Alberto Luis | |
dc.date.accessioned | 2022-03-09T08:38:14Z | |
dc.date.available | 2022-03-09T08:38:14Z | |
dc.date.issued | 2022-01-21 | |
dc.identifier.citation | Pedraz-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.uri | http://hdl.handle.net/10481/73242 | |
dc.description | We 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.abstract | Although 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.sponsorship | European Commission 765492 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | John Wiley & Sons | es_ES |
dc.rights | Atribución 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Biomarkers | es_ES |
dc.subject | Cancer | es_ES |
dc.subject | CircRNA | es_ES |
dc.subject | Diagnosis | es_ES |
dc.subject | nCounter | es_ES |
dc.subject | NSCLC | es_ES |
dc.title | Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens | es_ES |
dc.type | journal article | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/765492 | es_ES |
dc.rights.accessRights | open access | es_ES |
dc.identifier.doi | 10.1002/1878-0261.13182 | |
dc.type.hasVersion | VoR | es_ES |