Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis.

ABSTRACT: Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods,

SUBMITTER: He Z 

PROVIDER: S-EPMC7523182 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature