Abstract:
Medulloblastoma (MB) is the most prevalent pediatric brain tumor arising in the cerebellum. Since conventional therapies decrease life quality and cause deleterious effects on children, computer models are urgently required to simulate cancer phenotypes and determine potential therapeutic targets with minimum side effects on healthy cells. In the present study, metabolic alterations specific to MB were reflected on the brain genome-scale metabolic model by employing transcriptome data. Moreover, the relation between metastasis and the Warburg effects and the pathways utilized by MB without carbon source were investigated. Flux sampling analysis was also performed to detect statistically different reactions in healthy and MB cases. Regulation, flux coupling, and essentiality analyses were conducted as well to find therapeutic targets for MB. Additionally, the antimetabolites which might lessen the use of substrates in cells by causing competitive inhibition were identified by using similarity scores and conducting FBA. To investigate sphingolipid metabolism in depth, 79 reactions were newly included in the MB model. Consequently, the MB model captured metabolic characteristics of MB successfully as confirmed by experimental studies. It was found that targeting proteins/enzymes related to fatty acid synthesis, mevalonate pathway in cholesterol synthesis and inhibition of cardiolipin production, and tumor inducing sphingolipid metabolites might be beneficial therapeutic strategies for MB. Furthermore, the suppression of GABA catalyzing and succinate-producing enzymes simultaneously might be a potential solution for metastatic MB. Using oleic acid as an antimetabolite owing to its structural similarity to linoleate and its downregulation in MB might be also a promising approach for this life-threatening disease.
