Banca de QUALIFICAÇÃO: GUSTAVO LOVATTO MICHAELSEN

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : GUSTAVO LOVATTO MICHAELSEN
DATE: 07/06/2023
TIME: 09:30
LOCAL: Google Meet, meet.google.com/eop-hgwa-fmo
TITLE:

Construction and Validation of a Prognostic Gene Signature Utilizing Methylation Driven Genes in Medulloblastoma

KEY WORDS:

Medulloblastoma, prognostic biomarker, DNA methylation, precision medicine

PAGES: 30
BIG AREA: Ciências Biológicas
AREA: Biologia Geral
SUMMARY:

Hypermethylation of tumor suppressor genes and hypomethylation of oncogenes are methylation dysregulations crucial for cancer tumorigenesis and for tumor maintenance, and it is no exception for medulloblastoma (MB). MB is one of the most common pediatric brain tumors and it is estimated that one third of patients will die from the disease. Conventional prognostic parameters have limited and unreliable correlations with MB outcome. Considering the lack of accurate prognostic biomarkers is a major challenge for the clinical improvement of those patients, 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 1935 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. We used LASSO regression, a supervised machine learning statistical method, to the MDGs 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 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.

COMMITTEE MEMBERS:
Presidente - ***.016.910-** - MARIALVA SINIGAGLIA - ICI-RS
Interno - 1267860 - GUSTAVO ANTONIO DE SOUZA