Abstract
BACKGROUND: Glioblastoma multiforme (GBM), the most prevalent and aggressive primary brain tumor, poses substantial challenges in both treatment and prognosis. Post-translational modifications, like palmitoylation, are known to have critical roles in the development and progression of glioma. Yet, the molecular mechanisms involved in palmitoylation and its prognostic significance in GBM are still not fully understood. This study aimed to explore prognostic biomarkers for GBM based on palmitoylation-related genes and to construct a prognostic risk model. METHODS: The messenger ribonucleic acid (mRNA) expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to explore palmitoylation-related mechanisms in GBM. The Cox regression analysis was performed to identify prognostic palmitoylation-related genes and the consensus clustering was used for molecular classification. The package "limma" was used for differential gene expression analysis and the least absolute shrinkage and selection operator (LASSO) regression was applied to construct a risk signature. A nomogram model was established using the risk score and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. The difference in immune cell infiltration was compared between different risk groups. The drug susceptibility analysis and immunotherapy response prediction were conducted to access the ability of the risk signature in predicting the therapeutic effect. RESULTS: Based on datasets from TCGA, five palmitoylation-related genes were identified as prognostic markers, allowing for the categorization of GBM patients into two subtypes with differing survival rates. Through differential expression analysis, 570 specific genes linked to GBM advancement were uncovered. A total of seven signature genes (COL22A1, IGFBP6, SOD3, UPP1, CA14, TIMP4 and FERMT1) were applied to establish a prognostic risk model, which was demonstrated to be an independent prognostic indicator for patients with GBM. Kaplan-Meier analysis indicted that the GBM patients in low-risk group exhibited a better survival outcome compared the patients in high-risk group. The ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Two external cohort of patients from the GSE74187 and GSE83300 in the GEO database confirmed the model's strong predictive performance. The immune infiltration, drug sensitivity and immunotherapy responses were significantly different between the low- and high-risk groups. CONCLUSIONS: Our study offers insights into the molecular classification and prognostic assessment of GBM, focusing on palmitoylation-related mechanisms. The prognostic model we constructed provides valuable guidance for tailoring personalized treatment strategies for GBM patients.