A gene expression signature predicts recurrence-free survival in meningioma.

ABSTRACT:

Background

Meningioma is the most common primary brain tumor and has a variable risk of local recurrence. While World Health Organization (WHO) grade generally correlates with recurrence, there is substantial within-grade variation of recurrence risk. Current risk stratification does not accurately predict which patients are likely to benefit from adjuvant radiation therapy (RT). We hypothesized that tumors at risk for recurrence have unique gene expression profiles (GEP) that could better select patients for adjuvant RT.

Methods

We developed a recurrence predictor by machine learning modeling using a training/validation approach.

Results

Three publicly available AffymetrixU133 gene expression datasets (GSE9438, GSE16581, GSE43290) combining 127 primary, non-treated m

SUBMITTER: Olar A 

PROVIDER: S-EPMC5882319 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature