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

Area of Science:

• Oncology
• Metabolomics
• Genomics

Background:

• Lower-grade gliomas (LGGs) exhibit significant metabolic heterogeneity and adaptability.
• Understanding these metabolic differences is crucial for developing targeted therapeutic strategies.
• Defining distinct metabolic subtypes is necessary for effective treatment.

Purpose of the Study:

• To develop a novel classification system for lower-grade gliomas based on their metabolic gene expression profiles.
• To identify and characterize distinct metabolic subtypes within LGGs.
• To evaluate the clinical and molecular features associated with each metabolic subtype.

Main Methods:

• Utilized consensus clustering on metabolic gene expression data from 402 diffuse LGGs in The Cancer Genome Atlas (TCGA).
• Validated the classification system across three independent Chinese Glioma Genome Atlas (CGGA) cohorts.
• Integrated clinical, immune, metabolic, and somatic alteration data to characterize identified subtypes.

Main Results:

• Successfully identified and validated three distinct metabolic subtypes (M1, M2, M3) in LGGs.
• M2 subtype showed upregulated carbohydrate, nucleotide, and vitamin metabolism, correlating with worse prognosis.
• M1 subtype demonstrated upregulated lipid metabolism and immune infiltration, associated with better outcomes; M3 subtype exhibited low metabolic activity and good prognosis.
• Metabolic subtypes were linked to diverse somatic alterations and a validated prognostic signature.

Conclusions:

• The study introduces a novel classification system for LGGs based on metabolic gene expression.
• This classification highlights the significant metabolic heterogeneity within lower-grade gliomas.
• The findings provide a foundation for developing subtype-specific therapeutic strategies targeting metabolic pathways in LGGs.