# Metabolic reprogramming in clear cell renal cell carcinoma: core pathways and targeted therapeutic strategies

## TL;DR

This paper explores how ccRCC cancer cells rewire their metabolism and how this can be targeted with new therapies to improve treatment outcomes.

## Contribution

The paper introduces a stratified decision framework for ccRCC treatment based on metabolic and immune features, alongside translational strategies combining metabolic and immunotherapies.

## Key findings

- ccRCC metabolism involves intensified glycolysis, lipid droplet accumulation, and glutamine/one-carbon dependency.
- Metabolic heterogeneity, like the DCCD spectrum, influences prognosis and treatment response.
- Combining HIF-2α inhibitors with immunotherapies and lipid-targeting strategies shows promise in ccRCC treatment.

## Abstract

Clear cell renal cell carcinoma (ccRCC), rooted in VHL loss and dysregulated HIF signaling, is defined by a sweeping metabolic overhaul: intensified glycolysis, a “downshifted” TCA cycle, the buildup of lipid droplets and cholesteryl esters, and a pronounced dependence on glutamine and one-carbon metabolism—all tightly intertwined with an immunosuppressive microenvironment. Drawing on single-cell and spatial multi-omics, metabolomic and lipidomic profiling, and imaging-based evidence, this article maps the critical nodes of carbon, lipid, amino-acid, and one-carbon pathways, and their crosstalk with ferroptosis. It highlights how metabolic heterogeneity—exemplified by the DCCD spectrum—shapes prognosis and therapeutic response. The review further synthesizes how metabolic–immune coupling, including lipid metabolic rewiring in TAMs and MDSCs, and lactate/lipid stress in CD8+ T cells, contributes to immune-therapy resistance. On the translational front, HIF-2α inhibitors (such as belzutifan), strategies that suppress or oxidize lipids to trigger ferroptosis, and interventions targeting glutamine and one-carbon metabolism show promise when rationally combined with ICIs, TKIs, or anti-angiogenic therapies. We propose a stratified decision framework anchored in DCCD state, lipid-droplet/PLIN2 phenotype, ferroptosis sensitivity, and HIF activity, and discuss the emerging roles of radiopathomics (e.g., CT HU–PLIN2 coupling) and circulating metabolic fingerprints in companion diagnostics. Looking toward clinical deployment, advancing standardization within MSI/IBSI and FAIR data principles—and launching biomarker-enriched, prospective multicenter trials—will be essential to demonstrate the real-world value of precision metabolic oncology in the personalized treatment of ccRCC.