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

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dc.contributor.authorPeixoto, Carolina
dc.contributor.authorLopes, Marta B.
dc.contributor.authorMartins, Marta
dc.contributor.authorCosta, Luis
dc.contributor.authorVinga, Susana
dc.date.accessioned2021-04-07T11:35:13Z
dc.date.available2021-04-07T11:35:13Z
dc.date.issued2020
dc.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/).pt_PT
dc.description.abstractColorectal 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.pt_PT
dc.description.sponsorshipThis 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).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBiomedicines. 2020 Nov 10;8(11):488pt_PT
dc.identifier.doi10.3390/biomedicines8110488pt_PT
dc.identifier.eissn2227-9059
dc.identifier.urihttp://hdl.handle.net/10451/47272
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationIF/00409/2014pt_PT
dc.relationRegularized optimization for modeling RNA-seq oncological data
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relationNOVA Laboratory for Computer Science and Informatics
dc.relationCenter for Mathematics and Applications
dc.relationA machine learning-based forecasting system for shellfish safety
dc.relation.publisherversionhttps://www.mdpi.com/journal/biomedicinespt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRegularized optimizationpt_PT
dc.subjectCox regressionpt_PT
dc.subjectSurvival analysispt_PT
dc.subjectTCGA datapt_PT
dc.subjectRNA-seq datapt_PT
dc.titleTCox : correlation-based regularization applied to colorectal cancer survival datapt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleRegularized optimization for modeling RNA-seq oncological data
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardTitleNOVA Laboratory for Computer Science and Informatics
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleA machine learning-based forecasting system for shellfish safety
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/PD%2FBD%2F139146%2F2018/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04516%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00297%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FCCI-CIF%2F29877%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0026%2F2019/PT
oaire.citation.issue11pt_PT
oaire.citation.titleBiomedicinespt_PT
oaire.citation.volume8pt_PT
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream9471 - RIDTI
oaire.fundingStream3599-PPCDT
person.familyNamePeixoto
person.familyNameMartins
person.familyNameCosta
person.givenNameCarolina
person.givenNameMarta
person.givenNameLuis
person.identifier.ciencia-id2F1E-5CC7-294D
person.identifier.ciencia-id041E-4ADE-FB64
person.identifier.orcid0000-0002-7958-850X
person.identifier.orcid0000-0003-0429-9380
person.identifier.orcid0000-0002-4782-7318
person.identifier.scopus-author-id55268246400
project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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