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A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma.
Michaelsen, Gustavo Lovatto; da Silva, Lívia Dos Reis Edinger; de Lima, Douglas Silva; Jaeger, Mariane da Cunha; Brunetto, André Tesainer; Dalmolin, Rodrigo Juliani Siqueira; Sinigaglia, Marialva.
Afiliación
  • Michaelsen GL; Children's Cancer Institute, Porto Alegre, 90620-110, RS, Brazil.
  • da Silva LDRE; Bioinformatics Multidisciplinary Environment-BioME, Digital Metropole Institute, Federal University of Rio Grande do Norte, Natal, 59076-550, RN, Brazil.
  • de Lima DS; National Science and Technology Institute for Children's Cancer Biology and Pediatric Oncology - INCT BioOncoPed, Porto Alegre, 90035-003, RS, Brazil.
  • Jaeger MDC; Children's Cancer Institute, Porto Alegre, 90620-110, RS, Brazil.
  • Brunetto AT; Federal University of Health Sciences of Porto Alegre, Porto Alegre, 90050-170, RS, Brazil.
  • Dalmolin RJS; Children's Cancer Institute, Porto Alegre, 90620-110, RS, Brazil.
  • Sinigaglia M; Institute of Basic Health Sciences, Federal University of Rio Grande do Sul, Porto Alegre, 90035-003, RS, Brazil.
J Mol Neurosci ; 74(2): 47, 2024 Apr 25.
Article en En | MEDLINE | ID: mdl-38662144
ABSTRACT
Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. 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. In this study, we used six datasets totaling 1679 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. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived 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. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomarcadores de Tumor / Neoplasias Cerebelosas / Metilación de ADN / Transcriptoma / Meduloblastoma Límite: Child / Child, preschool / Female / Humans / Male Idioma: En Revista: J Mol Neurosci Asunto de la revista: BIOLOGIA MOLECULAR / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biomarcadores de Tumor / Neoplasias Cerebelosas / Metilación de ADN / Transcriptoma / Meduloblastoma Límite: Child / Child, preschool / Female / Humans / Male Idioma: En Revista: J Mol Neurosci Asunto de la revista: BIOLOGIA MOLECULAR / NEUROLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos