Background: Low-grade gliomas (LGG) account for 20-25% of all gliomas. In this study, we assessed whether metabolic status was correlated with clinical outcomes in LGG patients using data from The Cancer Genome Atlas (TCGA).
Methods:LGG patient data were collected from TCGA, and the Molecular Signature Database was used to extract gene sets related to energy metabolism. After performing a consensus-clustering algorithm, theLGG patients were divided into four clusters. We then compared the tumor prognosis, function, immune cell infiltration, checkpoint proteins, chemo-resistance, and cancer stem cells (CSC) between the two groups with the greatest prognostic difference. Using least absolute shrinkage and selection operator (LASSO) analysis, an energy metabolism-related signature was further developed.Results: Energy metabolism-related signatures were applied to identify four clusters (C1, C2, C3, and C4) using a consensus-clustering algorithm. C1 LGG patients were more related to the synapse and had higher CSC scores, more chemo-resistance, and a better prognosis. C4 LGG was observed to have more immunerelated pathways and better immunity. We then identified six energy metabolism-related genes (PYGL, HS3ST3B, NNMT, FMOD, CHST6, and B3GNT7) that can accurately predict LGG prognosis not only as a whole but also based on the independent predictions of each of these six genes.
Conclusions:The energy metabolism-related subtypes of LGG were identified, which were strongly related to the immune microenvironment, immune checkpoint proteins, CSCs, chemo-resistance, prognosis, and LGG advancement. A signature of genes involved in energy metabolism could help to distinguish and predict the prognosis of LGG patients, and a promising method to discover patients that may benefit fromLGG therapy.