TCox : correlation-based regularization applied to colorectal cancer survival data

Detalhes bibliográficos
Autor(a) principal: Peixoto, Carolina
Data de Publicação: 2020
Outros Autores: Lopes, Marta B., Martins, Marta, Costa, Luis, Vinga, Susana
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/47272
Resumo: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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spelling TCox : correlation-based regularization applied to colorectal cancer survival dataRegularized optimizationCox regressionSurvival analysisTCGA dataRNA-seq data© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology.This work was partially supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references PD/BD/139146/2018, IF/00409/2014, UIDB/50021/2020 (INESC-ID), UIDB/50022/2020 (IDMEC), UIDB/04516/2020 (NOVA LINCS), and UIDB/00297/2020 (CMA) and projects PREDICT (PTDC/CCI-CIF/29877/2017) and MATISSE (DSAIPA/DS/0026/2019).MDPIRepositório da Universidade de LisboaPeixoto, CarolinaLopes, Marta B.Martins, MartaCosta, LuisVinga, Susana2021-04-07T11:35:13Z20202020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/47272engBiomedicines. 2020 Nov 10;8(11):48810.3390/biomedicines81104882227-9059info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:50:05Zoai:repositorio.ul.pt:10451/47272Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:59:21.347561Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv TCox : correlation-based regularization applied to colorectal cancer survival data
title TCox : correlation-based regularization applied to colorectal cancer survival data
spellingShingle TCox : correlation-based regularization applied to colorectal cancer survival data
Peixoto, Carolina
Regularized optimization
Cox regression
Survival analysis
TCGA data
RNA-seq data
title_short TCox : correlation-based regularization applied to colorectal cancer survival data
title_full TCox : correlation-based regularization applied to colorectal cancer survival data
title_fullStr TCox : correlation-based regularization applied to colorectal cancer survival data
title_full_unstemmed TCox : correlation-based regularization applied to colorectal cancer survival data
title_sort TCox : correlation-based regularization applied to colorectal cancer survival data
author Peixoto, Carolina
author_facet Peixoto, Carolina
Lopes, Marta B.
Martins, Marta
Costa, Luis
Vinga, Susana
author_role author
author2 Lopes, Marta B.
Martins, Marta
Costa, Luis
Vinga, Susana
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Peixoto, Carolina
Lopes, Marta B.
Martins, Marta
Costa, Luis
Vinga, Susana
dc.subject.por.fl_str_mv Regularized optimization
Cox regression
Survival analysis
TCGA data
RNA-seq data
topic Regularized optimization
Cox regression
Survival analysis
TCGA data
RNA-seq data
description © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01T00:00:00Z
2021-04-07T11:35:13Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/47272
url http://hdl.handle.net/10451/47272
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Biomedicines. 2020 Nov 10;8(11):488
10.3390/biomedicines8110488
2227-9059
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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