Constructing a pancancer ubiquitination regulatory network to determine tumor characteristics, immunotherapy...

Ubiquitination-a pivotal post-translational modification that orchestrates cellular homeostasis and oncogenic pathways-remains underexplored as a pancancer regulatory hub. Although ubiquitination dysregulation is linked to tumor progression, a comprehensive, multicancer framework integrating prognostic, molecular, and microenvironmental landscapes is lacking.

This study integrated data of 4,709 patients from 26 cohorts across five solid tumor types (lung cancer, esophageal cancer, cervical cancer, urothelial cancer, and melanoma) and mapped the molecular profiles to the interaction network. Cox regression and the Kaplan-Meier survival method were employed for prognostic analysis. Functional enrichment and protein-protein interaction analyses were performed to identify the key downstream pathways and genes. Findings were validated using independent patient cohorts, cell line models, and in vivo experiments.

Key nodes and prognostic pathways within the ubiquitination-modification network were identified. A conserved ubiquitination-related prognostic signature (URPS) effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes across all analyzed cancers. URPS may serve as a novel biomarker for predicting immunotherapy response, with the potential to identify patients who are more likely to benefit from immunotherapy in clinical settings. A comprehensive analysis of URPS-associated proteins revealed novel cancer-related interaction partners as potential drug targets. At the single-cell resolution, URPS enabled more precise classification of distinct cell types and was associated with macrophage infiltration within the tumor microenvironment. In vivo, in vitro, and patient cohort analyses, demonstrated that OTUB1-TRIM28 ubiquitination plays a crucial role in modulating MYC pathway and influencing patient prognosis.

We constructed a pancancer ubiquitination regulatory network and prognostic model, revealing important pathways, and offering insights into predicting patient prognosis and understanding biological mechanisms.