Machine learning-driven identification of a ubiquitination-related prognostic signature and potential target NRDP1 in bladder cancer

Ubiquitination-regulated molecular events exert crucial regulatory effects on the initiation and progression of bladder cancer (BCa), yet clinically actionable ubiquitination-related prognostic models are still in short supply. This study is designed to construct a ubiquitination-associated prognostic signature for BCa via machine learning strategies. Ten machine learning algorithms with 101 parameter combinations analyzed BCa transcriptomes. The prognostic model was validated using time-dependent receiver operating characteristic curves, Kaplan-Meier survival analysis, nomogram construction, and multivariate Cox regression. To elucidate underlying biological functions, we conducted immune microenvironment profiling (via CIBERSORT algorithm) and gene set enrichment analysis (GSEA). Molecular docking experiments were performed using PubChem compound libraries, Protein Data Bank structures, and the CB-DOCK2 platform to screen potential drug targets. Subsequent experimental validation included Polymerase Chain Reaction (PCR), cell migration assays (Transwell and wound healing assays), and cell proliferation evaluations (colony formation and CCK-8 assays). Transcriptomic analysis identified 106 ubiquitination-related differentially expressed genes (DEGs) specific to BCa, among which 9 prognosis-related genes were filtered out via univariate Cox regression analysis. Our machine learning-based ubiquitination-related prognosis-associated signature showed superior performance over conventional clinical predictors, enabling accurate stratification of BCa patients into high- and low-risk subgroups. Computational docking results revealed strong binding affinities between the E3 ubiquitin ligase NRDP1 and classic agents. Notably, targeted knockdown of NRDP1 substantially impaired the migration capabilities of BCa cells. This study introduces a BCa machine learning derived prognostic model, identifies NRDP1 as a key BCa prognostic and therapeutic target, and highlights the value of integrating machine learning and ubiquitination biology.