Memorias de investigación
Artículos en revistas:
Identification of a biomarker panel for colorectal cancer diagnosis
Año:2012
Áreas de investigación
  • Inteligencia artificial,
  • Biomedicina,
  • Bioinformática
Datos
Descripción
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. 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. 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. 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).
Internacional
Si
JCR del ISI
Si
Título de la revista
Bmc Cancer
ISSN
1471-2407
Factor de impacto JCR
3,153
Información de impacto
Datos JCR del año 2010
Volumen
12
DOI
doi:10.1186/1471-2407-12-43
Número de revista
43
Desde la página
1
Hasta la página
13
Mes
SIN MES
Ranking
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Participantes
  • Autor: Amaia García-Bilbao Fundación GAIKER
  • Autor: Ruben Armañanzas Arnedillo UPM
  • Autor: Ziortza Ispizua Fundación GAIKER
  • Autor: Begoña Calvo Hospital de Cruces
  • Autor: Ana Alonso-Varona Universidad del País Vasco
  • Autor: Iñaki Inza Cano Universidad del País Vasco
  • Autor: Pedro Maria Larrañaga Mugica UPM
  • Autor: Guillermo López-Vivanco Hospital de Cruces
  • Autor: Blanca Suárez-Merino Fundación GAIKER
  • Autor: Mónica Betanzos Fundación GAIKER
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial