Identification of a Prognostic Risk-Scoring Model Based on Amino Acid Metabolism in Renal Clear Cell Carcinoma

Abstract

Background: Amino acid metabolism (AAM) plays a significant role in the biological processes of cancer. However, the role of AAM in terms of prognosis, tumor-related immunity, and therapy effectiveness in patients with clear cell renal cell carcinoma (ccRCC) has not been thoroughly explored. Therefore, the present study aimed to explore the role of AAM in ccRCC from the aforementioned aspects. Methods: In this study, we used RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) to construct an AAM-related risk model (AAMRSM) for ccRCC. We employed the least absolute shrinkage and selection operator (LASSO), Cox regression analyses, and random forest algorithm to develop the model. Results: We discovered a strong correlation between the AAMRS and prognosis (p < 0.05). The nomogram, which was built using the AAMRS and several clinical parameters, demonstrated significant predictive power. In addition, individuals categorized by the AAMRS showed distinguishable immune status, T-cell-related immune factors, tumor mutation burden (TMB), and medical sensitivity (p < 0.05). Conclusions: In conclusion, the association between AAMRS and prognosis, immunological landscape, and therapy effectiveness has been established. The use of AAMRSM can aid doctors in selecting more individualized and accurate treatments for patients with ccRCC.