Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/160879 |
Resumo: | 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, multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA) are integrated, while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. It was possible to find a set of highly correlated features distinguishing glioblastoma from low- grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients were identified, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets. |
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Unveiling Novel Glioma Biomarkers through Multi-omics Integration and ClassificationCanonical Correlation AnalysisClassificationGliomaMulti-omicsDomínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e TecnologiasGlioma 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, multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA) are integrated, while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. It was possible to find a set of highly correlated features distinguishing glioblastoma from low- grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients were identified, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets.O glioma é atualmente um dos tipos mais prevalentes de cancro cerebral primário. Dado o seu elevado nível de heterogeneidade e dada a complexidade dos seus marcadores moleculares biológicos, muitos esforços têm sido realizados para classificar com precisão o tipo de glioma em cada paciente, o que, por sua vez, é fundamental para melhorar o diagnóstico precoce e aumentar a sobrevivência. No entanto, como resultado dos avanços tecnológicos em rápido crescimento na sequenciação de dados e na evolução da com- preensão molecular da biologia do glioma, a sua classificação foi recentemente sujeita a alterações significativas. Neste estudo, múltiplas modalidades ómicas de glioma (in- cluindo mRNA, metilação de DNA e miRNA) provenientes do The Cancer Genome Atlas (TCGA) são integradas, juntamente com a utilização das classes revistas e reclassificadas de glioma, com um método supervisionado baseado em análise de correlação canónica esparsa (DIABLO) para discriminar entre os tipos de glioma. Foi possível encontrar um conjunto de características altamente correlacionadas que distinguem o glioblastoma dos gliomas de baixo grau (LGG) que estavam principalmente associadas à ruptura das vias de sinalização dos receptores de tirosina quinases e à organização e remodelação da matriz extracelular. Por outro lado, a discriminação dos tipos LGG foi caracterizada principalmente por variáveis envolvidas nos processos de ubiquitinação e transcrição de DNA. Além disso, foram identificados vários novos biomarcadores de glioma potencial- mente úteis tanto no diagnóstico quanto no prognóstico dos pacientes, incluindo os genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A e HEPN1. No geral, este método de classificação permitiu discriminar com desempenho muito elevado os diferentes pacientes com glioma, simultaneamente procurando informações comuns entre os vários tipos de dados, permitindo, em última análise, a compreensão de mecanis- mos essenciais que impulsionam a heterogeneidade em glioma e revelam potenciais alvos terapêuticos.Lopes, MartaBispo, ReginaRUNVieira, Francisca Manuel Gaspar2023-12-05T12:35:32Z2023-112023-11-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/160879enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:43:39Zoai:run.unl.pt:10362/160879Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:58:15.411949Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
title |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
spellingShingle |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification Vieira, Francisca Manuel Gaspar Canonical Correlation Analysis Classification Glioma Multi-omics Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
title_short |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
title_full |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
title_fullStr |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
title_full_unstemmed |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
title_sort |
Unveiling Novel Glioma Biomarkers through Multi-omics Integration and Classification |
author |
Vieira, Francisca Manuel Gaspar |
author_facet |
Vieira, Francisca Manuel Gaspar |
author_role |
author |
dc.contributor.none.fl_str_mv |
Lopes, Marta Bispo, Regina RUN |
dc.contributor.author.fl_str_mv |
Vieira, Francisca Manuel Gaspar |
dc.subject.por.fl_str_mv |
Canonical Correlation Analysis Classification Glioma Multi-omics Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
topic |
Canonical Correlation Analysis Classification Glioma Multi-omics Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
description |
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, multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA) are integrated, while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. It was possible to find a set of highly correlated features distinguishing glioblastoma from low- grade gliomas (LGG) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. On the other hand, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients were identified, including the genes PPP1R8, GPBP1L1, KIAA1614, C14orf23, CCDC77, BVES, EXD3, CD300A and HEPN1. Overall, this classification method allowed to discriminate the different TCGA glioma patients with very high performance, while seeking for common information across multiple data types, ultimately enabling the understanding of essential mechanisms driving glioma heterogeneity and unveiling potential therapeutic targets. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-05T12:35:32Z 2023-11 2023-11-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/160879 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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