A Novel Scoring Model of Deubiquitination Patterns Predicts Prognosis and Immunotherapeutic Response in Hepatocellular Carcinoma

Aberrant expression of deubiquitinases (DUBs) is significantly associated with tumorigenesis. However, the precise impact of deubiquitination on the tumour microenvironment (TME) and immunotherapy in hepatocellular carcinoma (HCC) remains unclear. In this study, we comprehensively characterized the transcriptional and genetic alterations of 26 overall survival (OS)-related DUBs in HCC. The consensus clustering algorithm was used to identify patients with distinct deubiquitination patterns. We then established a DUBscore model using the principal component analysis (PCA) algorithm to quantify the deubiquitination patterns of individual HCC patients. Finally, we performed weighted gene coexpression network analysis (WGCNA) to identify the key DUBs. Consequently, three distinct deubiquitination patterns were identified, each showing significant differences in the characteristics of the TME, immune response, and clinical prognosis. Further analysis revealed that the DUBscore was an independent prognostic factor and could predict the response to immunotherapy for patients with HCC. Ultimately, BRCC3 was identified as a key DUB based on the DUBscore, which was significantly overexpressed in tumour tissues, as confirmed by qRT‒PCR and immunohistochemistry (IHC). We analysed the distribution and expression of BRCC3 in various types of immune cells using single-cell RNA sequencing (scRNA-seq). In conclusion, our study revealed the crucial role of deubiquitination patterns in shaping TME complexity and diversity. A more personalized and effective antitumour immunotherapy strategy can be developed by utilizing the DUBscore model to identify deubiquitination patterns in individual HCC patients. Our findings also highlight that BRCC3 may serve as a potential therapeutic target in HCC and a predictive marker for immunotherapeutic response.