Banca de DEFESA: GUSTAVO LOVATTO MICHAELSEN

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : GUSTAVO LOVATTO MICHAELSEN
DATE: 14/09/2023
TIME: 11:00
LOCAL: meet.google.com/cju-unso-zhp
TITLE:

Construction and Validation of a Prognostic Model Integrating Gene Expression and DNA Methylation Data in Medulloblastoma

KEY WORDS:

medulloblastoma · prognostic biomarker · DNA methylation · precision medicine

PAGES: 57
BIG AREA: Ciências Biológicas
AREA: Biologia Geral
SUMMARY:

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 accurateprognostic biomarkers is a major challenge for the clinical improvement of thosepatients, 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 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
Interna - 1365498 - BEATRIZ STRANSKY FERREIRA
Externa à Instituição - CAROLINA NOR