BACKGROUND

Arginine methylation, a key post-translational modification, plays a pivotal role in regulating various cellular processes and has been implicated in cancer progression. However, the potential of arginine methylation-related genes as prognostic markers in clear cell renal cell carcinoma (ccRCC) remains underexplored.

METHODS

We utilized public transcriptomic datasets from TCGA, E-MTAB-1980 and ICGC, for model construction and validation. Single-cell RNA sequencing datasets were employed to evaluate gene expression patterns at the cellular level. Consensus clustering, KM survival analysis, and GSVA were applied to identify molecular subtypes and related pathways. Univariate and multivariate Cox regression analyses were applied to develop an arginine methylation-related signature (AMS). Immune profiling, mutation landscape, and drug sensitivity prediction were also employed to explore the model's association with clinical features, immune infiltration, mutation burden, and therapeutic responses.

RESULTS

The AMS demonstrated robust prognostic performance, with consistent validation across external cohorts. High-risk patients exhibited significantly worse survival, elevated TMB, and an immunosuppressive tumor microenvironment characterized by increased infiltration of regulatory immune cells. Single-cell RNA sequencing revealed key prognostic genes expressed predominantly in cancer and immune cells, supporting their role in tumor progression and immune interactions.

CONCLUSION

The arginine methylation-based prognostic model provides a reliable framework for survival risk stratification in ccRCC and holds promise for guiding personalized therapeutic strategies. Future research should emphasize clinical validation of this model and explore its potential role in optimizing immunotherapy and targeted treatment strategies for ccRCC.