Comprehensive analysis of metabolic patterns in renal cell carcinoma: implications for prognosis and treatment

Renal cell carcinoma (RCC) presents significant clinical and molecular heterogeneity, which makes prognosis and treatments very complicated. Despite advances in surgical and systemic therapies, a substantial number of RCC patients progress to advanced stages, highlighting the need for novel stratification approaches that account for the tumor's biological complexity.

An integrative multi-omic analysis, combining transcriptomic and clinical data, was performed to identify the metabolic subtypes of RCC. Unsupervised clustering was used to stratify patients based on their metabolic profiles, and subtype-specific molecular signatures were examined through differential expression and pathway enrichment analyses. Prognostic outcomes, immune features, and drug sensitivities were then analyzed. The value of the classification was validated by the biological experiments.

Three distinct metabolic subtypes (C1, C2, and C3) were identified, each associated with distinct survival outcomes. The C1 subtype, marked by enhanced oxidative phosphorylation and fatty acid metabolism, correlated with improved survival. The C2 subtype, characterized by prostaglandin biosynthesis, was linked to poor prognosis and immune evasion. The C3 subtype was similar to C2 but was characterized by extensive prostanoid biosynthesis, indicating a moderate prognosis in the three subtypes. Immunotherapy and targeted drug sensitivity analyses revealed subtype-specific vulnerabilities, suggesting potential therapeutic strategies tailored to each metabolic profile. Subsequent in vitro assays confirmed the significance of targets to the RCC biological process.

Metabolic subtyping through multi-omics integration offers a clinically relevant framework for RCC prognosis and personalized treatment. This approach highlights the role of metabolic reprogramming in tumor immunity and therapeutic response, providing a foundation for future clinical applications in precision oncology.