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

Research output: Contribution to journalArticle

Ida Larsson, Mathias Uhlén, Cheng Zhang, Adil Mardinoglu

Original languageEnglish
Article number381
JournalFrontiers in Genetics
Volume11
DOIs
Publication statusPublished - 17 Apr 2020

King's Authors

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 find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.

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