Construção e validação de um modelo prognóstico integrando dados de expressão gênica e metilação de DNA em Meduloblastoma
Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will die from the disease. The lack of accurate prognostic biomarkers is a major challenge for the clinical improvement of those patients, with conventional prognostic parameters h...
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| Formato: | Dissertação |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/55709 |
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| Resumo: | Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is
estimated that one-third of patients will die from the disease. The lack of accurate
prognostic biomarkers is a major challenge for the clinical improvement of those patients,
with conventional prognostic parameters having limited and unreliable correlations with the
disease outcome. Acknowledging this issue, our aim was to build a gene signature and
evaluate its potential as a new prognostic model for patients with the disease.
Hypermethylation of tumor suppressor genes and hypomethylation of oncogenes are
methylation dysregulations crucial for cancer tumorigenesis and tumor maintenance, and it
is no exception for MB. In this study, we used six datasets totaling 1679 MB samples,
including RNA gene expression and DNA methylation data from primary MB as well as
control samples from healthy cerebellum. We identified methylation-driven genes (MDGs)
in MB, genes whose expression is correlated with their methylation and which are also
differentially methylated in relation to healthy tissue. After, LASSO regression, a
supervised machine learning statistical method, was used with the MDGs as a parameter
resulting in a two-gene signature (GS-2) of candidate prognostic biomarkers for MB
(CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall
survival (OS) with Kaplan-Meier analysis (log-rank p < 0.01). We evaluated its robustness
and accuracy with receiver operating characteristic (ROC) curves predicting OS at 1, 3 and
5 years in multiple datasets (training set: 77.2%, 73.2% and 71.2%, mean in three validation
sets: 83.6%, 77.6%, 75.4% at 1, 3 and 5 years respectively). We evaluated the GS-2 as an
independent prognostic biomarker with multivariable Cox regression which showed p-value
< 0.01 in all four datasets evaluated. The methylation-regulated GS-2 risk score model can
effectively classify patients with MB into high and low-risk, reinforcing the importance of
this epigenetic modification in the disease. Such genes stand out as promising prognostic
biomarkers with potential application for MB treatment. |
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