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Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods
Publication . Martins, Sofia; Coletti, Roberta; Lopes, Marta B.; DI - Departamento de Informática; CMA - Centro de Matemática e Aplicações; NOVALincs; UNIDEMI - Unidade de Investigação e Desenvolvimento em Engenharia Mecânica e Industrial; DEMI - Departamento de Engenharia Mecânica e Industrial; BioMed Central (BMC)
Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups’ separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.
Classification and biomarker selection in lower-grade glioma using robust sparse logistic regression applied to RNA-seq data
Publication . Carrilho, João F.; Lopes, Marta B.; DM - Departamento de Matemática; NOVALincs; CMA - Centro de Matemática e Aplicações; Universidade Federal de Lavras -Departamento de Estatistica
Effective diagnosis and treatment in cancer is a barrier for the development of personalized medicine, mostly due to tumor heterogeneity. In the particular case of gliomas, highly heterogeneous brain tumors at the histological, cellular and molecular levels, and exhibiting poor prognosis, the mechanisms behind tumor heterogeneity and progression remain poorly understood. The recent advances in biomedical high-throughput technologies have allowed the generation of large amounts of molecular information from the patients that combined with statistical and machine learning techniques can be used for the definition of glioma subtypes and targeted therapies, an invaluable contribution to disease understanding and effective management. In this work sparse and robust sparse logistic regression models with the elastic net penalty were applied to glioma RNA-seq data from The Cancer Genome Atlas (TCGA), to identify relevant tran-scriptomic features in the separation between lower-grade glioma (LGG) subtypes and identify putative outlying observations. In general, all classification models yielded good accuracies, selecting different sets of genes. Among the genes selected by the models, TXNDC12, TOMM20, PKIA, CARD8 and TAF12 have been reported as genes with relevant role in glioma development and progression. This highlights the suitability of the present approach to disclose relevant genes and fosters the biological validation of non-reported genes.
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
Concurso para Financiamento de Projetos de Investigação Científica e Desenvolvimento Tecnológico em Todos os Domínios Científicos - 2020
Número da atribuição
PTDC/CCI-BIO/4180/2020