Identification and validation of SUN modification-related anti-PD-1 immunotherapy-resistance signatures to predict prognosis and immune microenvironment status in glioblastoma.

BACKGROUND: Ubiquitination, SUMOylation, and neddylation (collectively termed SUN modifications) play crucial roles in cancer pathogenesis and immunotherapy resistance. This study investigated the prognostic significance of these modifications in glioblastoma (GBM). METHODS: Key genes associated with SUN modifications and anti-PD-1 resistance were identified using integrated bioinformatic approaches, including differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms. The expression levels of identified genes were subsequently validated in GBM cell lines using RT-qPCR and Western blotting. A prognostic risk model was constructed based on the key genes. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptome analysis were further employed to characterize gene expression patterns. RESULTS: Six prognostic genes (PLK2, CDC73, PSMC2, SOCS3, ETV4, and LMO7) were identified. CDC73, PSMC2, SOCS3, and ETV4 were upregulated, while PLK2 and LMO7 were downregulated in GBM cells. The six-gene prognostic risk model demonstrated excellent predictive performance, achieving an Area Under the Curve (AUC) exceeding 0.9. The derived risk score exhibited significant correlations with clinical features, immune infiltration levels, and drug sensitivity profiles. Furthermore, scRNA-seq and spatial transcriptome analysis revealed high SOCS3 expression specifically in monocytes and macrophages, suggesting its potential role in mediating the activity of these immune cells to influence tumor progression and drug sensitivity in GBM. CONCLUSION: This study established a robust six-gene prognostic model related to SUN modifications and anti-PD-1 therapy in GBM. The model demonstrates strong predictive ability and correlates with clinically relevant parameters, highlighting its potential utility for survival prediction and guiding therapeutic management decisions in GBM patients.