Clear cell renal cell carcinoma: immunological significance of alternative splicing signatures - Zhang, Jiayu; Jiang, Hongyi; Rao, Dapang; Jin, Xishi (Frontiers in Oncology, 2024)
Background: Renal cell carcinoma (RCC) accounts for 90% of renal cancers, of which clear cell carcinoma (ccRCC) is the most usual histological type. The process of alternative splicing (AS) contributes to protein diversity, and the dysregulation of protein diversity may have a great influence in tumorigenesis. We developed a prognostic signature and comprehensively analyzed the role of tumor immune microenvironment (TIME) and immune checkpoint blocking (ICB) treatment in ccRCC.Methods: To identify prognosis-related AS events, univariate Cox regression was used and functional annotation was performed using gene set enrichment analysis (GSEA). In this study, prognostic signatures were developed based on multivariate Cox, univariate Cox and LASSO regression models. And to assess prognostic value, there were such used methods as proportional hazards model, Kruskal-Wallis analysis, and ROC curves. To obtain a better understanding of TIME in ccRCC, the ESTIMATE R package, Single sample gene set enrichment analysis (ssGSEA) algorithm, CIBERSORT method, and the tumor immune estimation resource (TIMER) were applied. The database was searched to verify the expression of C4ORF19 in tumor and normal samples. Regulatory networks for AS-splicing factors (SFs) were visualized using Cytoscape 3.9.1.Results: There were 9347 AS cases associated with survival of ccRCC patients screened. A total of eight AS prognostic signatures were developed with stable prognostic predictive accuracy based on splicing subtypes. In addition, a qualitative prognostic nomogram was developed and the prognostic prediction showed high effectiveness. In addition, we found that combined signature was significantly associated with the diversity of TIME and ICB treatment related genes. C4ORF19 might become an important prognostic factor for ccRCC. At last, the AS-SF regulatory network was established to clearly reveal the potential function of splicing factors (SFs).Conclusion: We found novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of ccRCC. A new and reliable prognostic nomogram was established to quantitatively predict the clinical outcome. The AS-SF networks could provide a new way for the study of potential regulatory mechanisms. And important roles of AS events in context of TIME and immunotherapy efficiency were exhibited. C4ORF19 was found to be a vital gene in TIME and ICB treatment.