Identification of an energy metabolism-related six-gene signature for distinguishing and forecasting the prognosis of low-grade gliomas.

ABSTRACT:

Background

Low-grade gliomas (LGG) account for 20-25% of all gliomas. In this study, we assessed whether metabolic status was correlated with clinical outcomes in LGG patients using data from The Cancer Genome Atlas (TCGA).

Methods

LGG patient data were collected from TCGA, and the Molecular Signature Database was used to extract gene sets related to energy metabolism. After performing a consensus-clustering algorithm, the LGG patients were divided into four clusters. We then compared the tumor prognosis, function, immune cell infiltration, checkpoint proteins, chemo-resistance, and cancer stem cells (CSC) between the two groups with the greatest prognostic difference. Using least absolute shrinkage and selection operator (LASSO) analysis, an energy metabolism-related signat

SUBMITTER: Liu G 

PROVIDER: S-EPMC9951020 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature