Inferring Diagnostic and Prognostic Gene Expression Signatures Across WHO Glioma Classifications

Coletti, Roberta; Leiria de Mendonça, Mónica; Vinga, Susana; Lopes, Marta B.

http://hdl.handle.net/10362/179279

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Autores

Coletti, Roberta

Leiria de Mendonça, Mónica

Vinga, Susana

Lopes, Marta B.

Resumo(s)

Tumor 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.

Descrição

Funding 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.

Palavras-chave

biomarkers glioma graphical lasso networks regularized cox regression RNA-Seq survival analysis TCGA variable selection WHO CNS classification Biochemistry Molecular Biology Computer Science Applications Computational Mathematics Applied Mathematics SDG 3 - Good Health and Well-being