AI Article Synopsis

  • Glioblastoma multiforme (GBM) is the most aggressive type of malignant brain tumor, accounting for 81% of such tumors, and alters metabolic pathways for cancer cell survival and spread.
  • This study introduces a new approach to assess metabolic subpathways in GBM, identifying 25 significantly abnormal pathways that could be targeted for treatment.
  • A key finding is that (S)-2,3-Epoxysqualene can inhibit GBM cell activity and induce cell death, indicating its potential as a therapeutic agent; the method used for this research is also available in an open-source R package on GitHub.

Article Abstract

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7533644PMC
http://dx.doi.org/10.3389/fonc.2020.01549DOI Listing

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