Abstract
BACKGROUND: Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Palmitoylation plays a key role in the development and progression of GBM, but its molecular mechanism and prognostic significance in GBM are still not fully understood. This study aims to explore the prognostic biomarkers of GBM based on palmitoylation-related genes. METHODS: Eight scoring methods, including AUCell, UCell, singscore, ssGSEA, JASMINE, VAM, scSE, and viper, were used to score each sample. In addition, the palmitoylation score calculated by the AUCell algorithm is selected as the representative. In order to screen the genes related to GBM survival and build a risk prognosis model, 101 algorithms constructed by 10 kinds of machine learning are arranged and combined for variable screening and model building, and then immune infiltration and immunotherapy evaluation, drug screening, and molecular docking are carried out. RESULTS: We observed that macrophages in GBM cell types have the highest palmitoylation score. The secondary dimensionality reduction clustering of macrophages showed that the palmitoylation of PLCG2 + macrophages was significantly higher than that of other subtypes, and three core prognostic genes (ZDHHC2, ZDHHC4, ZDHHC20) were screened out by machine learning. A higher risk score is significantly related to worse clinical status and most immune labels. Among them, ZDHHC2 was significantly up-regulated in GBM in several verification groups. Molecular docking found that quercetin was the best targeted drug for ZDHHC2. CONCLUSION: This study revealed for the first time the heterogeneity of palmitoylation at the GBM single-cell level. The identification of ZDHHC2, ZDHHC4, and ZDHHC20 as key regulators of palmitoylation in GBM emphasized their potential as biomarkers and therapeutic targets.