Decoding lactylation heterogeneity in glioblastoma: machine learning identifies G6PC3 as a prognostic target

Area of Science:

• Oncology
• Biochemistry
• Genomics

Background:

• Glioblastoma (GBM) is a deadly brain cancer with poor prognosis.
• Novel therapeutic strategies are urgently needed to improve GBM patient survival.
• Lactylation, a post-translational modification, influences tumor progression but is understudied in GBM.

Purpose of the Study:

• To investigate lactylation heterogeneity in GBM using single-cell and bulk sequencing data.
• To identify key genes and pathways associated with lactylation in GBM.
• To explore the potential of lactylation-associated genes as prognostic biomarkers and therapeutic targets in GBM.

Main Methods:

• Integrated single-cell and bulk GBM sequencing data (TCGA-GBM, CGGA325, CGGA693).
• Developed a single-cell lactylation-associated gene expression score (LAGES) for subpopulation analysis.
• Employed machine learning algorithms and Cox regression to identify prognostic genes, followed by in vitro validation.

Main Results:

• LAGES stratified GBM cells into high- and low-expression groups with distinct functional profiles (e.g., invasion, immune suppression).
• Identified G6PC3 as a core gene associated with lactylation, correlating with glioma grade and poor prognosis.
• G6PC3 knockdown inhibited GBM cell proliferation, migration, and invasion in vitro.

Conclusions:

• Lactylation heterogeneity in GBM was characterized, identifying G6PC3 as a key gene.
• G6PC3 shows potential as a preliminary prognostic biomarker and a target for precision therapy in GBM.
• Further research is needed to validate findings and explore therapeutic applications of lactylation-targeted inhibitors.