Abstract
BACKGROUND: Previous studies have demonstrated a close association between neddylation modifications and tumor progression as well as alterations in the microenvironment. This study aimed to explore the role of neddylation in bladder cancer (BLCA) progression and its prognostic significance. METHODS: Gene expression data from the TCGA database were analyzed to identify neddylation gene modules using the limma software package and weighted gene co-expression network analysis (WGCNA). Prognostic models based on neddylation-related genes were subsequently developed through LASSO and Cox regression analyses. Additionally, a protein-protein interaction (PPI) network, gene set enrichment analysis (GSEA), and BLCA single-cell sequencing data were utilized to explore the functional roles of hub genes in BLCA and their impact on biological pathways. The expression of these hub genes was further validated in clinical samples via RT-qPCR. RESULTS: WGCNA analysis revealed 1412 neddylation-related hub genes. LASSO and Cox regression analyses subsequently identified six key genes: CUL1, PUM2, UBE2D3, HIF3A, COPS2, and DDB1. Transcriptomic data and RT-qPCR findings indicated that PUM2 and HIF3A exhibited high expression levels in normal tissues, while DDB1 showed increased expression in tumor tissues; no significant changes were observed for CUL1, COPS2, and UBE2D3. By integrating these gene expressions with significant clinical features, a prognostic model was constructed that demonstrated excellent diagnostic efficiency (AUC: 0.793 at 1 year, 0.792 at 3 years, and 0.773 at 5 years). In addition, single cell sequencing highlighted the potential role of these genes in modulating immune responses and mediating interactions between tumor cells and immune cells. GSEA also suggested that DDB1 may play a crucial role in orchestrating key biological processes associated with BLCA, particularly in activating apoptotic signaling pathways. CONCLUSION: The six neddylation-related genes (CUL1, PUM2, UBE2D3, HIF3A, COPS2, and DDB1) emerge as potential independent indicators of survival in patients with BLCA, and the constructed survival models exhibit significant diagnostic efficacy.