Abstract
OBJECTIVES: Glioma, the most common and aggressive form of brain cancer, possesses a complex biology, which makes elucidating its underlying mechanisms and developing effective treatment strategies challenging. Lactylation is a recently discovered post-translational modification and has emerged as a novel research target to understand its role in various biological processes and diseases. Herein, we explored the role of lactylation in gliomas. METHODS: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the Tumour Immune Single-Cell Hub database. The R package 'Seurat' was used for processing the scRNA-seq data. Lactylation-related genes were identified from published literature and the Molecular Signatures Database. An unsupervised clustering method was used to identify glioma subtypes based on identified lactylation-related genes. Differences among the various clusters were examined, including clinical features, differentially expressed genes (DEGs), enriched pathways and immune cell infiltrates. A lactylation score was generated to predict the overall survival (OS) of patients with glioma using DEGs between the two clusters. RESULTS: The lactylation-related genes were obtained from the scRNA-seq data, identifying two molecular subtypes, and a prognostic signature was established to stratify patients with glioma into high- and low-score groups. Analysis of the tumour immune microenvironment revealed that patients in the high-score group exhibited increased immune cell infiltration, chemokine expression and immune checkpoint expression but exhibited worse OS and better response to immunotherapy. CONCLUSIONS: Altogether, we established a novel signature based on lactylation-related clusters for robust survival prediction and immunotherapeutic response in gliomas.