Digital multiplexed analysis of circular RNAs in FFPE and fresh non-small cell lung cancer specimens
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 pl...
| Autores: | , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:292942 |
| Acceso en línea: | https://ddd.uab.cat/record/292942 https://dx.doi.org/urn:doi:10.1002/1878-0261.13182 |
| Access Level: | acceso abierto |
| Palabra clave: | Biomarkers Cancer Circrna Diagnosis Ncounter NSCLC |
| Sumario: | 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 paraffin-embedded (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. Aberrant circular RNA (circRNA) expression is present in lung cancer. Using nCounter with machine learning, we discovered two signatures able to discriminate FFPE lung cancer samples from controls even at early stage. Our results not only highlight the potential of circRNAs as lung cancer biomarkers but also introduce nCounter as a suitable platform for circRNA expression studies in these samples. |
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