Metabolic expression profiling stratifies diffuse lower-grade glioma into three distinct tumour subtypes

Background Lower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs. Methods The metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype. Results We successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction. Conclusions Our study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs.

Automated summary

The study identified three highly distinct metabolic subtypes in LGGs, correlated with different metabolic activities such as carbohydrate, nucleotide, vitamin, and lipid metabolism. Each subtype showed differences in prognosis and somatic alterations. Furthermore, a metabolic signature with better prognosis prediction performance was developed.