Identification of a novel autophagy-related prognostic signature and small molecule drugs for glioblastoma by bioinformatics.

ABSTRACT:

Objective

To explore the autophagy-related prognostic signature (ARPs) via data mining in gene expression profiles for glioblastoma (GBM).

Methods

Using the Cancer Genome Atlas (TCGA) database, we obtained 156 GBM samples and 5 adjacent normal samples, and denoted them as discovery cohort. Univariate Cox regression analysis was used to screen autophagy genes that related to GBM prognosis. Then, the least absolute shrinkage and selection operator Cox regression model was used to construct an autophagy-based ARPs, which was validated in an external cohort containing 80 GBM samples. The patients in the above-mentioned cohorts were divided into low-risk group and high-risk group according to the median prognostic risk score, and the diagnostic performance of the model was assess

SUBMITTER: Wang D 

PROVIDER: S-EPMC9097333 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature