2026-06-14T02:20:48Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/166612025-05-23T15:07:26Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_15322col_20.500.12105_19608col_20.500.12105_16962col_20.500.12105_16970col_20.500.12105_16974col_20.500.12105_16983col_20.500.12105_16990
Repisalud
author
Gómez-Acebo, Inés
author
Llorca, Javier
author
Alonso-Molero, Jéssica
author
Díaz-Martínez, Marta
author
Perez-Gomez, Beatriz
author
Amiano, Pilar
author
Belmonte, Thalia
author
Molina, Antonio J
author
Burgui, Rosana
author
Castaño-Vinyals, Gemma
author
Moreno, Victor
author
Molina-Barceló, Ana
author
Marcos-Gragera, Rafael
author
Kogevinas, Manolis
author
Pollan-Santamaria, Marina
author
Dierssen-Sotos, Trinidad
funder
Instituto de Salud Carlos III
funder
Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
funder
Fundación Marqués de Valdecilla
funder
International Cancer Genome Consortium
funder
Ministerio de Economía y Competitividad (España)
funder
Red Temática de Investigación Cooperativa en Cáncer (RTICC) (España)
funder
Junta de Castilla y León (España)
funder
Regional Government of Andalusia (España)
funder
Generalitat Valenciana (España)
funder
Fundación La Caixa
funder
Basque Government (España)
funder
Gobierno de la Región de Murcia (España)
funder
Asociación Española Contra el Cáncer
funder
Government of Catalonia (España)
funder
Agència de Gestió d´Ajuts Universitaris i de Recerca (AGAUR)
funder
Fundación Caja de Ahorros de Asturias
funder
University of Oviedo (España)
funder
Ministerio de Ciencia e Innovación. Centro de Excelencia Severo Ochoa (España)
funder
Ministerio de Economía (España)
funder
Unión Europea. Comisión Europea
2023-11-14T10:11:46Z
2023-11-14T10:11:46Z
2023-11-04
Eur J Med Res. 2023 Nov 4;28(1):480.
10.1186/s40001-023-01471-2
2047-783X
European journal of medical research
37925534
http://hdl.handle.net/20.500.12105/16661
Purpose: To build models combining circulating microRNAs (miRNAs) able to identify women with breast cancer as well as different types of breast cancer, when comparing with controls without breast cancer. Method: miRNAs analysis was performed in two phases: screening phase, with a total n = 40 (10 controls and 30 BC cases) analyzed by Next Generation Sequencing, and validation phase, which included 131 controls and 269 cases. For this second phase, the miRNAs were selected combining the screening phase results and a revision of the literature. They were quantified using RT-PCR. Models were built using logistic regression with LASSO penalization. Results: The model for all cases included seven miRNAs (miR-423-3p, miR-139-5p, miR-324-5p, miR-1299, miR-101-3p, miR-186-5p and miR-29a-3p); which had an area under the ROC curve of 0.73. The model for cases diagnosed via screening only took in one miRNA (miR-101-3p); the area under the ROC curve was 0.63. The model for disease-free cases in the follow-up had five miRNAs (miR-101-3p, miR-186-5p, miR-423-3p, miR-142-3p and miR-1299) and the area under the ROC curve was 0.73. Finally, the model for cases with active disease in the follow-up contained six miRNAs (miR-101-3p, miR-423-3p, miR-139-5p, miR-1307-3p, miR-331-3p and miR-21-3p) and its area under the ROC curve was 0.82. Conclusion: We present four models involving eleven miRNAs to differentiate healthy controls from different types of BC cases. Our models scarcely overlap with those previously reported.
eng
Breast cancer
Screening
miRNA
Diagnosis
Prognosis
Circulating miRNAs signature on breast cancer: the MCC-Spain project
research article
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