Multi-omics analysis reveals the role of ribosome biogenesis in malignant clear cell renal cell carcinoma and the development of a machine learning-based prognostic model.

Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, marked by high molecular heterogeneity and limited responsiveness to targeted or immune therapies. Ribosome biogenesis (Ribosis), a central regulator of cell growth and metabolism, has emerged as a driver of tumor aggressiveness. However, its role in ccRCC pathogenesis and prognosis remains poorly defined. Methods: We integrated bulk RNA sequencing, single-cell RNA sequencing, and spatial transcriptomics sequencing data to dissect the biological functions and clinical relevance of Ribosis-related genes in ccRCC. Through pseudotime trajectory analysis and metabolic flux inference, we examined malignant progression and metabolic reprogramming. A prognostic model based on a Ribosis-related signature (RBRS) was built using 118 machine learning algorithm combinations and validated in internal and external cohorts. A web-based calculator was also developed. We further analyzed immune infiltration, genomic alterations, tumor microenvironment features, and drug sensitivity. Expression of five core Ribosis-related genes (RPL38, RPS2, RPS14, RPS19, RPS28) was validated by qRT-PCR. Results: We identified a Ribosis-high malignant subpopulation with enhanced stemness, poor prognosis, and elevated oxidative phosphorylation. These cells showed increased metabolic activity, especially in the pyruvate-lactate axis, potentially facilitating immune evasion. The RBRS model outperformed 32 published signatures (C-index = 0.68). High-risk patients exhibited an "immune-activated yet immunosuppressed" microenvironment, with increased CD8 Conclusion: We reveal Ribosis as a key driver of ccRCC progression. The RBRS model demonstrates robust prognostic value and translational utility, linking Ribosis to metabolism, immune dysfunction, and therapy resistance, offering new insights for risk stratification and precision treatment in ccRCC.