Metabolic Profiles Point Out Metabolic Pathways Pivotal in Two Glioblastoma (GBM) Cell Lines, U251 and U-87MG.
Filipa MartinsDavid van der KellenLuís G GonçalvesJacinta SerpaPublished in: Biomedicines (2023)
Glioblastoma (GBM) is the most lethal central nervous system (CNS) tumor, mainly due to its high heterogeneity, invasiveness, and proliferation rate. These tumors remain a therapeutic challenge, and there are still some gaps in the GBM biology literature. Despite the significant amount of knowledge produced by research on cancer metabolism, its implementation in cancer treatment has been limited. In this study, we explored transcriptomics data from the TCGA database to provide new insights for future definition of metabolism-related patterns useful for clinical applications. Moreover, we investigated the impact of key metabolites (glucose, lactate, glutamine, and glutamate) in the gene expression and metabolic profile of two GBM cell lines, U251 and U-87MG, together with the impact of these organic compounds on malignancy cell features. GBM cell lines were able to adapt to the exposure to each tested organic compound. Both cell lines fulfilled glycolysis in the presence of glucose and were able to produce and consume lactate. Glutamine dependency was also highlighted, and glutamine and glutamate availability favored biosynthesis observed by the increase in the expression of genes involved in fatty acid (FA) synthesis. These findings are relevant and point out metabolic pathways to be targeted in GBM and also reinforce that patients' metabolic profiling can be useful in terms of personalized medicine.
Keyphrases
- single cell
- gene expression
- end stage renal disease
- healthcare
- fatty acid
- systematic review
- signaling pathway
- chronic kidney disease
- newly diagnosed
- squamous cell carcinoma
- dna methylation
- type diabetes
- poor prognosis
- ejection fraction
- mass spectrometry
- stem cells
- skeletal muscle
- peritoneal dialysis
- blood pressure
- quality improvement
- cell therapy
- high resolution
- deep learning
- cell wall