Prognostic prediction and immune checkpoint profiling in glioma patients through neddylation-associated features.

Background: Gliomas are the most common primary malignant tumours of the central nervous system, and neddylation may be a potential target for the treatment of gliomas. Our study analysed neddylation's potential role in gliomas of different pathological types and its correlation with immunotherapy.

Methods: Genes required for model construction were sourced from existing literature, and their expression data were extracted from the TCGA and CGGA databases. LASSO regression was employed to identify genes associated with the prognosis of glioma patients in TCGA and to establish a clinical prognostic model. Biological changes in glioma cell lines following intervention with hub genes were evaluated using the CCK-8 assay and transwell assay. The genes implicated in the model construction were validated across various cell lines using Western blot. We conducted analyses to examine correlations between model scores and clinical data, tumor microenvironments, and immune checkpoints. Furthermore, we investigated potential differences in molecular functions and mechanisms among different groups.

Results: We identified 249 genes from the Reactome database and analysed their expression profiles in the TCGA and CGGA databases. After using LASSO-Cox, four genes (BRCA1, BIRC5, FBXL16 and KLHL25, p < 0.05) with significant correlations were identified. We selected FBXL16 for validation in in vitro experiments. Following FBXL16 overexpression, the proliferation, migration, and invasion abilities of glioma cell lines all showed a decrease. Then, we constructed the NEDD Index for gliomas. The nomogram indicated that this model could serve as an independent prognostic marker. Analysis of the tumour microenvironment and immune checkpoints revealed that the NEDD index was also correlated with immune cell infiltration and the expression levels of various immune checkpoints.

Conclusion: The NEDD index can serve as a practical tool for predicting the prognosis of glioma patients, and it is correlated with immune cell infiltration and the expression levels of immune checkpoints.