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).
Highlights
The incidence of glioblastoma multiforme (GBM) accounts for 65% of all types of gliomas, and glioblastoma is the highest grade of glioma according to the World Health Organization (WHO) classification
As shown in the metabolic subpathway analysis workflow (Figure 1), our study was mainly composed of three parts: (i) we first used the iSubpathwayMiner system to disassemble the KEGG metabolic pathway into connected metabolic subpathways based on the k-clique algorithm
In order to better identify the abnormal metabolic pathways of GBM, we developed a method based on the metabolic subpathway activity score matrix
Summary
The incidence of GBM accounts for 65% of all types of gliomas, and glioblastoma is the highest grade of glioma according to the World Health Organization (WHO) classification. With the popularity and reduced cost of high-throughput sequencing technologies, high-throughput sequencing data related to malignant gliomas continue to emerge How to use these data to comprehensively demonstrate the changes involved in the disease mechanism in a cost-effective manner has become a popular research direction [5]. Through the application of data mining algorithms in biology, genes associated with GBM are constantly being discovered (e.g., COL3A1, FN1, and MMP9) [6] Limitations, such as poor stability and the lack of consideration of the biological relationships between genes, make the analysis at the genetic level questionable. ESEA (Edge Set Enrichment Analysis) identifies dysregulated pathways by investigating the changes in biological relationships of pathways in the context of gene expression data [8]. Subpathways are used as signature, biomarkers, and drug recognition of cancer [13, 15, 16]
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