Identification and validation of a novel robust glioblastoma prognosis model based on bioinformatics.

ABSTRACT:

Background

Glioblastoma (GBM) is a very common primary malignant tumor of the central nervous system (CNS). Aging, macrophage, autophagy, and methylation related genes are hypothesized to be crucial to its pathogenesis. In this study, we aimed to explore the role of these genes in the prognosis of GBM.

Methods

The RNA sequence (RNA-seq) and clinical information were downloaded from The Cancer Genome Atlas database (TCGA) and the Chinese Glioma Genome Atlas database (CGGA). We performed univariate and least absolute shrinkage and selection operator (LASSO) multivariate Cox regression analysis to identify risk signatures related to overall survival (OS). We further developed a nomogram to predict individual outcomes. In addition, the immune microenvironment was analyzed by CIB

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11414505 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature