Abstract
BACKGROUND: Emerging perspectives on tumor metabolism reveal its heterogeneity, a characteristic yet to be fully explored in gliomas. To advance therapies targeting metabolic processes, it is crucial to uncover metabolic differences and identify distinct metabolic subtypes. Therefore, we aimed to develop a classification system for gliomas based on the enrichment levels of four key metabolic pathways: glutaminolysis, glycolysis, the pentose phosphate pathway, and fatty acid oxidation. METHODS: Energy-related features of glioma were characterized through integrative analyses of multiple datasets, including bulk, single-cell, and spatial transcriptome profiling. The glioma energy metabolic subtypes were constructed using the R package ConsensusClusterPlus. Kaplan-Meier analysis was conducted to compare clinical outcomes between different metabolic groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to elucidate the biological functions of genes of interest. Cell-cell communication analysis was performed at single-cell resolution using the R package CellChat and at spatial resolution using the standard stLearn pipeline. RESULTS: Glioma samples were stratified into two prognostic subtypes. Group 1, enriched in the glutaminolysis pathway, had better clinical outcomes. In contrast, Group 2 exhibited high activities in glycolysis, the pentose phosphate pathway, and fatty acid oxidation, correlating with decreased survival time. Group 1 samples were predominantly located in the peripheral region and had a high composition of neuron cells. Group 2, however, had increased infiltration of tumor-promoting immune cells, such as M2 macrophages, and was characterized by traits of invasion, hypoxia, and immunity. Lastly, cell-cell communications were compared across different tumor regions, and the CX3CL1/CX3CR1 ligand-receptor pair was validated using spatial transcriptomic data. CONCLUSIONS: Our work revealed the metabolic heterogeneity in glioma by developing a new classification system with significant prognostic and therapeutic value. Single-cell transcriptional profiles offer novel insights into tumor metabolic reprogramming, which could enhance therapies tailored to cell- or patient-specific metabolic patterns.