Identification of a biomarker panel for colorectal cancer diagnosis

García Bilbao, Amaia and Armañanzas Arnedillo, Ruben and Ispizua, Ziortza and Calvo, Begoña and Alonso Varona, Ana and Inza Cano, Iñaki and Larrañaga Múgica, Pedro and López Vivanco, Guillermo and Suárez Merino, Blanca and Betanzos, Mónica (2012). Identification of a biomarker panel for colorectal cancer diagnosis. "Bmc Cancer", v. 12 (n. 43); pp. 1-13. ISSN 1471-2407.


Title: Identification of a biomarker panel for colorectal cancer diagnosis
  • García Bilbao, Amaia
  • Armañanzas Arnedillo, Ruben
  • Ispizua, Ziortza
  • Calvo, Begoña
  • Alonso Varona, Ana
  • Inza Cano, Iñaki
  • Larrañaga Múgica, Pedro
  • López Vivanco, Guillermo
  • Suárez Merino, Blanca
  • Betanzos, Mónica
Item Type: Article
Título de Revista/Publicación: Bmc Cancer
Date: 2012
Volume: 12
Faculty: Facultad de Informática (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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)

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Item ID: 13937
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Deposited by: Memoria Investigacion
Deposited on: 21 Dec 2012 11:37
Last Modified: 21 Apr 2016 13:22
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