Feature selection and survival modeling in The Cancer Genome Atlas

Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the co...

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Autores principales:	Kim H, Bredel M
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2013
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/9c42fd775da845d4bb93535c9aa5eb06
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spelling	oai:doaj.org-article:9c42fd775da845d4bb93535c9aa5eb062021-12-02T05:10:26ZFeature selection and survival modeling in The Cancer Genome Atlas1176-91141178-2013https://doaj.org/article/9c42fd775da845d4bb93535c9aa5eb062013-09-01T00:00:00Zhttp://www.dovepress.com/feature-selection-and-survival-modeling-in-the-cancer-genome-atlas-a14361https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. Patients and methods: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1) 1-nearest neighbor (1-NN) survival prediction method; (2) random patient selection method and a Cox-based regression method with nested cross-validation; (3) least absolute shrinkage and selection operator (LASSO) optimization using whole-genome gene expression profiles; or (4) gene expression profiles of cancer pathway genes. Results: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone. Conclusion: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology. Keywords: brain, feature selection, glioblastoma, personalized medicine, survival modeling, TCGAKim HBredel MDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2013, Iss Supplement 1 Nanoinformatics, Pp 57-62 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Kim H Bredel M Feature selection and survival modeling in The Cancer Genome Atlas
description	Hyunsoo Kim,1 Markus Bredel2 1Department of Pathology, The University of Alabama at Birmingham, Birmingham, AL, USA; 2Department of Radiation Oncology, and Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL, USA Purpose: Personalized medicine is predicated on the concept of identifying subgroups of a common disease for better treatment. Identifying biomarkers that predict disease subtypes has been a major focus of biomedical science. In the era of genome-wide profiling, there is controversy as to the optimal number of genes as an input of a feature selection algorithm for survival modeling. Patients and methods: The expression profiles and outcomes of 544 patients were retrieved from The Cancer Genome Atlas. We compared four different survival prediction methods: (1) 1-nearest neighbor (1-NN) survival prediction method; (2) random patient selection method and a Cox-based regression method with nested cross-validation; (3) least absolute shrinkage and selection operator (LASSO) optimization using whole-genome gene expression profiles; or (4) gene expression profiles of cancer pathway genes. Results: The 1-NN method performed better than the random patient selection method in terms of survival predictions, although it does not include a feature selection step. The Cox-based regression method with LASSO optimization using whole-genome gene expression data demonstrated higher survival prediction power than the 1-NN method, but was outperformed by the same method when using gene expression profiles of cancer pathway genes alone. Conclusion: The 1-NN survival prediction method may require more patients for better performance, even when omitting censored data. Using preexisting biological knowledge for survival prediction is reasonable as a means to understand the biological system of a cancer, unless the analysis goal is to identify completely unknown genes relevant to cancer biology. Keywords: brain, feature selection, glioblastoma, personalized medicine, survival modeling, TCGA
format	article
author	Kim H Bredel M
author_facet	Kim H Bredel M
author_sort	Kim H
title	Feature selection and survival modeling in The Cancer Genome Atlas
title_short	Feature selection and survival modeling in The Cancer Genome Atlas
title_full	Feature selection and survival modeling in The Cancer Genome Atlas
title_fullStr	Feature selection and survival modeling in The Cancer Genome Atlas
title_full_unstemmed	Feature selection and survival modeling in The Cancer Genome Atlas
title_sort	feature selection and survival modeling in the cancer genome atlas
publisher	Dove Medical Press
publishDate	2013
url	https://doaj.org/article/9c42fd775da845d4bb93535c9aa5eb06
work_keys_str_mv	AT kimh featureselectionandsurvivalmodelinginthecancergenomeatlas AT bredelm featureselectionandsurvivalmodelinginthecancergenomeatlas
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Feature selection and survival modeling in The Cancer Genome Atlas

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