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Tcox: Correlation-based regularization applied to colorectal cancer survival data

2020-11Artigo científico Acesso aberto
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dc.contributor.authorPeixoto, Carolina
dc.contributor.authorLopes, Marta B.
dc.contributor.authorMartins, Marta
dc.contributor.authorCosta, Luís
dc.contributor.authorVinga, Susana
dc.contributor.institutionNOVALincs
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.pblMDPI AG
dc.date.accessioned2021-01-06T23:55:46Z
dc.date.available2021-01-06T23:55:46Z
dc.date.issued2020-11
dc.descriptionThis work was partially supported by national funds through Fundacao para a Ciencia 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).
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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent16
dc.format.extent2735228
dc.identifier.doi10.3390/biomedicines8110488
dc.identifier.issn2227-9059
dc.identifier.otherPURE: 27348678
dc.identifier.otherPURE UUID: a980924a-b3d3-49b4-b4cf-720f2aeff480
dc.identifier.otherScopus: 85096059296
dc.identifier.otherPubMed: 33182598
dc.identifier.otherPubMedCentral: PMC7696515
dc.identifier.otherWOS: 000592778200001
dc.identifier.otherORCID: /0000-0002-4135-1857/work/86346406
dc.identifier.urihttp://hdl.handle.net/10362/109860
dc.identifier.urlhttps://www.scopus.com/pages/publications/85096059296
dc.language.isoeng
dc.peerreviewedyes
dc.subjectCox regression
dc.subjectRegularized optimization
dc.subjectRNA-seq data
dc.subjectSurvival analysis
dc.subjectTCGA data
dc.subjectMedicine (miscellaneous)
dc.subjectGeneral Biochemistry,Genetics and Molecular Biology
dc.subjectSDG 3 - Good Health and Well-being
dc.titleTcox: Correlation-based regularization applied to colorectal cancer survival dataen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue11
degois.publication.lastPage16
degois.publication.titleBiomedicines
degois.publication.volume8
dspace.entity.typePublication
rcaap.rightsopenAccess

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