Construction of a prognostic model using lactylation-related genes for predicting the prognosis of glioma patients

Gliomas are the most lethal malignant tumors of the central nervous system, and their treatment continues to face serious challenges. Increasing evidence suggests that lactylation is strongly associated with tumorigenesis and progression. However, studies of lactylation in gliomas are rare. In this study, we screened for lactylation-related genes in glioblastoma affecting patient prognosis based on TCGA and GEO databases and constructed a prediction model for lactylation-related genes using various machine learning methods. In addition, the researchers have also performed tumor somatic mutation difference analysis, drug sensitivity analysis, and single-cell analysis. We developed a prognostic model for lactylation-related genes and validated its predictive power. Further analysis revealed differences in tumor somatic mutations between the high- and low-risk groups. We screened 50 drugs using drug sensitivity analysis, and at the single-cell level, we demonstrated the expression of characterized genes in glioblastoma. Our findings suggest that the lactylation-related gene prediction model can serve as a reliable tool for predicting the prognosis of patients with glioma.