dc.contributor.author | García Bilbao, Amaia | |
dc.contributor.author | Armañanzas Arnedillo, Rubén | |
dc.contributor.author | Ispizua, Ziortza | |
dc.contributor.author | Calvo Hernaez, Begoña | |
dc.contributor.author | Alonso Varona, Ana Isabel | |
dc.contributor.author | Inza Cano, Iñaki | |
dc.contributor.author | Larrañaga, Pedro | |
dc.contributor.author | López Vivanco, Guillermo María | |
dc.contributor.author | Suárez Merino, Blanca | |
dc.contributor.author | Betanzos, Mónica | |
dc.date.accessioned | 2012-04-11T18:11:02Z | |
dc.date.available | 2012-04-11T18:11:02Z | |
dc.date.issued | 2012-01-26 | |
dc.identifier.citation | BMC Cancer 12(43) : (2012) | es |
dc.identifier.issn | 1471-2407 | |
dc.identifier.uri | http://hdl.handle.net/10810/7317 | |
dc.description.abstract | Background: Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.
Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by
hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60- mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.
Results: After an exhaustive process of pre-processing to ensure data quality–lost values imputation, probes quality, data smoothing and intraclass variability filtering–the final dataset comprised a total of 8, 104 probes. Next,
a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.
Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997
and 0.955). | es |
dc.description.sponsorship | This work was supported by the Etortek and Saiotek 2003-2007 programs (Basque
Government). AGB was supported by Fundación Iñaki Goenaga Fellowship for graduate studies. RA was supported by the Spanish Ministry of Science and Innovation through a Juan de la Cierva postdoctoral fellowship,
TIN2010-20900-C04-04 and the Cajal Blue Brain Project. BC was supported by
the Spanish Ministry of Health (Miguel Servet 03/0062). II was supported by
the Spanish Ministry of Science and Innovation (TIN2010-14931) and
COMBIOMED network of the Carlos III Institute. | es |
dc.language.iso | eng | es |
dc.publisher | BioMed Central | es |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.subject | biomarker | es |
dc.subject | colorectal cancer | es |
dc.subject | diagnosis | es |
dc.title | Identification of a biomarker panel for colorectal cancer diagnosis | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2012 Garcia-Bilbao et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | es |
dc.relation.publisherversion | http://www.biomedcentral.com/1471-2407/12/43 | es |
dc.identifier.doi | 10.1186/1471-2407-12-43 | |
dc.departamentoes | Biología celular e histología | es_ES |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | es_ES |
dc.departamentoeu | Zelulen biologia eta histologia | es_ES |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala | es_ES |
dc.subject.categoria | GENETICS AND HEREDITY | |
dc.subject.categoria | ONCOLOGY | |