Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers

Bibliographic Details
Main Author: Vieira, Francisca G.
Publication Date: 2024
Other Authors: Bispo, Regina, Lopes, Marta B.
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/179276
Summary: Publisher Copyright: © The Author(s) 2024.
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network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
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spelling Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel BiomarkersCanonical correlation analysisclassificationgliomamulti-omicssurvival analysisBiochemistryMolecular BiologyComputer Science ApplicationsComputational MathematicsApplied MathematicsSDG 3 - Good Health and Well-beingPublisher Copyright: © The Author(s) 2024.Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A, and HEPN1. Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.CMA - Centro de Matemática e AplicaçõesDM - Departamento de MatemáticaUNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e IndustrialRUNVieira, Francisca G.Bispo, ReginaLopes, Marta B.2025-02-18T21:19:54Z2024-01-012024-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/179276eng1177-9322PURE: 106562666https://doi.org/10.1177/11779322241249563info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-02-24T01:48:55Zoai:run.unl.pt:10362/179276Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T20:40:11.656261Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
title Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
spellingShingle Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
Vieira, Francisca G.
Canonical correlation analysis
classification
glioma
multi-omics
survival analysis
Biochemistry
Molecular Biology
Computer Science Applications
Computational Mathematics
Applied Mathematics
SDG 3 - Good Health and Well-being
title_short Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
title_full Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
title_fullStr Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
title_full_unstemmed Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
title_sort Integration of Multi-Omics Data for the Classification of Glioma Types and Identification of Novel Biomarkers
author Vieira, Francisca G.
author_facet Vieira, Francisca G.
Bispo, Regina
Lopes, Marta B.
author_role author
author2 Bispo, Regina
Lopes, Marta B.
author2_role author
author
dc.contributor.none.fl_str_mv CMA - Centro de Matemática e Aplicações
DM - Departamento de Matemática
UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial
RUN
dc.contributor.author.fl_str_mv Vieira, Francisca G.
Bispo, Regina
Lopes, Marta B.
dc.subject.por.fl_str_mv Canonical correlation analysis
classification
glioma
multi-omics
survival analysis
Biochemistry
Molecular Biology
Computer Science Applications
Computational Mathematics
Applied Mathematics
SDG 3 - Good Health and Well-being
topic Canonical correlation analysis
classification
glioma
multi-omics
survival analysis
Biochemistry
Molecular Biology
Computer Science Applications
Computational Mathematics
Applied Mathematics
SDG 3 - Good Health and Well-being
description Publisher Copyright: © The Author(s) 2024.
publishDate 2024
dc.date.none.fl_str_mv 2024-01-01
2024-01-01T00:00:00Z
2025-02-18T21:19:54Z
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/10362/179276
url http://hdl.handle.net/10362/179276
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1177-9322
PURE: 106562666
https://doi.org/10.1177/11779322241249563
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.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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