Identification of an energy metabolism-related six-gene signature for distinguishing and forecasting the prognosis of low-grade gliomas

作者：Guoli Liu, Yuan Lu, Duangui Gao, Zhi Huang, Lin Ma · 发表于：Annals of Translational Medicine · 年份：2023 · DOI：10.21037/atm-22-6502 · 被引用次数：9 · 研究领域：Glioma Diagnosis and Treatment、Ferroptosis and cancer prognosis、Cancer, Hypoxia, and Metabolism

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, the LGG 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 immune-related 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 relat...