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Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications

2024-01-01Artigo científico Acesso aberto
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dc.contributor.authorColetti, Roberta
dc.contributor.authorLeiria de Mendonça, Mónica
dc.contributor.authorVinga, Susana
dc.contributor.authorLopes, Marta B.
dc.contributor.institutionCMA - Centro de Matemática e Aplicações
dc.contributor.institutionUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
dc.contributor.pblSAGE Publications
dc.date.accessioned2025-02-18T21:20:29Z
dc.date.available2025-02-18T21:20:29Z
dc.date.issued2024-01-01
dc.descriptionFunding Information: The results showed here are based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga . Publisher Copyright: © The Author(s) 2024.
dc.description.abstractTumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official World Health Organization (WHO) classification of the central nervous system (CNS). These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on The Cancer Genome Atlas (TCGA) glioma RNA-sequencing data set updated according to the 2016 and 2021 WHO guidelines, we proposed a 2-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularized Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent1367633
dc.identifier.doi10.1177/11779322241271535
dc.identifier.issn1177-9322
dc.identifier.otherPURE: 107198976
dc.identifier.otherPURE UUID: 41f7964f-450f-4736-90c4-2342172b58d9
dc.identifier.otherScopus: 85203967167
dc.identifier.otherORCID: /0000-0002-4135-1857/work/178390370
dc.identifier.urihttp://hdl.handle.net/10362/179279
dc.identifier.urlhttps://www.scopus.com/pages/publications/85203967167
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/CEEC INST 2ed/CEECINST%2F00042%2F2021%2FCP1773%2FCT0001/PT
dc.relationNot Available
dc.relationCenter for Mathematics and Applications
dc.relationCenter for Mathematics and Applications
dc.relationInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT
dc.relationMulti-omic networks in gliomas
dc.relationOLISSIPO – Fostering Computational Biology Research and Innovation in Lisbon
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-BIO%2F4180%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Base/UIDB%2F50022%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/951970/EU
dc.subjectbiomarkers
dc.subjectglioma
dc.subjectgraphical lasso
dc.subjectnetworks
dc.subjectregularized cox regression
dc.subjectRNA-Seq
dc.subjectsurvival analysis
dc.subjectTCGA
dc.subjectvariable selection
dc.subjectWHO CNS classification
dc.subjectBiochemistry
dc.subjectMolecular Biology
dc.subjectComputer Science Applications
dc.subjectComputational Mathematics
dc.subjectApplied Mathematics
dc.subjectSDG 3 - Good Health and Well-being
dc.titleInferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classificationsen
dc.title.subtitleA Network-Based Approachen
dc.typejournal article
degois.publication.titleBioinformatics and Biology Insights
degois.publication.volume18
dspace.entity.typePublication
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oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleCenter for Mathematics and Applications
oaire.awardTitleInstituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa
oaire.awardTitleMulti-omic networks in gliomas
oaire.awardTitleOLISSIPO – Fostering Computational Biology Research and Innovation in Lisbon
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oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-BIO%2F4180%2F2020/PT
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oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/951970/EU
oaire.fundingStreamCEEC INST 2ed
oaire.fundingStream6817 - DCRRNI ID
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oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Programático
oaire.fundingStream3599-PPCDT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Base
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