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Review
. 2022 Jun 28;23(13):7156.
doi: 10.3390/ijms23137156.

Glioblastoma and Methionine Addiction

Mark L Sowers  1   2 , Lawrence C Sowers  1   3
Affiliations
Review

Glioblastoma and Methionine Addiction

Mark L Sowers et al. Int J Mol Sci. .

Abstract

Glioblastoma is a fatal brain tumor with a bleak prognosis. The use of chemotherapy, primarily the alkylating agent temozolomide, coupled with radiation and surgical resection, has provided some benefit. Despite this multipronged approach, average patient survival rarely extends beyond 18 months. Challenges to glioblastoma treatment include the identification of functional pharmacologic targets as well as identifying drugs that can cross the blood-brain barrier. To address these challenges, current research efforts are examining metabolic differences between normal and tumor cells that could be targeted. Among the metabolic differences examined to date, the apparent addiction to exogenous methionine by glioblastoma tumors is a critical factor that is not well understood and may serve as an effective therapeutic target. Others have proposed this property could be exploited by methionine dietary restriction or other approaches to reduce methionine availability. However, methionine links the tumor microenvironment with cell metabolism, epigenetic regulation, and even mitosis. Therefore methionine depletion could result in complex and potentially undesirable responses, such as aneuploidy and the aberrant expression of genes that drive tumor progression. If methionine manipulation is to be a therapeutic strategy for glioblastoma patients, it is essential that we enhance our understanding of the role of methionine in the tumor microenvironment.

Keywords: epigenetics; glioblastoma; metabolism; methionine; therapeutic development; tumor microenvironment.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Methionine metabolic pathways.
Figure 2
Figure 2
Kaplan Meier curves of NNMT and MGMT microarray-based expression. High expression of either gene is associated with poor survival with a Wilcoxon p value of 1 × 10−4 and 7.1 × 10−3 for NNMT and MGMT, respectively. Data was obtained and visualized using the TCGA-GBM study Agilent-4502A microarray data. Optimal cutoff points for survival curves were determined using the survminer package, −1.93 and −1.39 for NNMT and MGMT, respectively. Visualization and statistics of this data were prepared using http://gliovis.bioinfo.cnio.es/ (accessed on 1 May 2022) [64].
Figure 3
Figure 3
NAD Synthesis by de novo and salvage pathways.
See this image and copyright information in PMC

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