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Twiner: correlation-based regularization for identifying common cancer gene signatures

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dc.contributor.authorLopes, Marta B.
dc.contributor.authorCasimiro, Sandra
dc.contributor.authorVinga, Susana
dc.date.accessioned2022-09-23T13:09:14Z
dc.date.available2022-09-23T13:09:14Z
dc.date.issued2019
dc.description© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.pt_PT
dc.description.abstractBackground: Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics. Results: We address the challenge of extracting gene signatures from transcriptomic data of prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) samples, particularly estrogen positive (ER+), and androgen positive (AR+) triple-negative breast cancer (TNBC), using sparse logistic regression. The introduction of gene network information based on the distances between BRCA and PRAD correlation matrices is investigated, through the proposed twin networks recovery (twiner) penalty, as a strategy to ensure similarly correlated gene features in two diseases to be less penalized during the feature selection procedure. Conclusions: Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.pt_PT
dc.description.sponsorshipThis work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/EEA/50008/2019 (Instituto de Telecomunicações), UID/CEC/50021/2019 (INESC-ID), UID/EMS/50022/2019 (IDMEC, LAETA), PREDICT (PTDC/CCI-CIF/29877/2017), and PERSEIDS (PTDC/EMS-SIS/0642/2014).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBMC Bioinformatics. 2019 Jun 25;20(1):356pt_PT
dc.identifier.doi10.1186/s12859-019-2937-8pt_PT
dc.identifier.eissn1471-2105
dc.identifier.urihttp://hdl.handle.net/10451/54563
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationInstituto de Telecomunicações
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationAssociate Laboratory of Energy, Transports and Aeronautics
dc.relationPERSEIDS - Personalizing cancer therapy through integrated modeling and decision
dc.relation.publisherversionhttps://bmcbioinformatics.biomedcentral.com/pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBreast invasive carcinomapt_PT
dc.subjectGene networkpt_PT
dc.subjectProstate adenocarcinomapt_PT
dc.subjectSparse logistic regressionpt_PT
dc.subjectTriple-negative breast cancerpt_PT
dc.titleTwiner: correlation-based regularization for identifying common cancer gene signaturespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleInstituto de Telecomunicações
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleAssociate Laboratory of Energy, Transports and Aeronautics
oaire.awardTitlePERSEIDS - Personalizing cancer therapy through integrated modeling and decision
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FCEC%2F50021%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEMS%2F50022%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FCCI-CIF%2F29877%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEMS-SIS%2F0642%2F2014/PT
oaire.citation.issue1pt_PT
oaire.citation.titleBMC Bioinformaticspt_PT
oaire.citation.volume20pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream9471 - RIDTI
oaire.fundingStream3599-PPCDT
person.familyNameB. Lopes
person.familyNameCara de Anjo Casimiro
person.familyNameVinga
person.givenNameMarta
person.givenNameSandra Cristina
person.givenNameSusana
person.identifier1195979
person.identifierB-8450-2008
person.identifier.ciencia-idFD16-A07F-7B12
person.identifier.ciencia-id0F12-5181-0B22
person.identifier.ciencia-id9713-F74D-4805
person.identifier.orcid0000-0002-4135-1857
person.identifier.orcid0000-0002-6917-4477
person.identifier.orcid0000-0002-1954-5487
person.identifier.ridF-5378-2011
person.identifier.scopus-author-id55489480400
person.identifier.scopus-author-id14043403400
person.identifier.scopus-author-id55893670600
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
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
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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