A four-gene signature-derived risk score for glioblastoma: prospects for prognostic and response predictive analyses.

ABSTRACT:

Objective

Glioblastoma (GBM) is the most common primary malignant brain tumor regulated by numerous genes, with poor survival outcomes and unsatisfactory response to therapy. Therefore, a robust, multi-gene signature-derived model is required to predict the prognosis and treatment response in GBM.

Methods

Gene expression data of GBM from TCGA and GEO datasets were used to identify differentially expressed genes (DEGs) through DESeq2 or LIMMA methods. The DEGs were then overlapped and used for survival analysis by univariate and multivariate COX regression. Based on the gene signature of multiple survival-associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan-Meier analysis and log-rank test. Gene set enrichmen

SUBMITTER: Cao M 

PROVIDER: S-EPMC6743613 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature