A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma.

ABSTRACT: Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the surviva

SUBMITTER: Qian C 

PROVIDER: S-EPMC9247148 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature