Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development.

ABSTRACT: Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to fi

SUBMITTER: Larsson I 

PROVIDER: S-EPMC7181968 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature