Lipid metabolism of clear cell renal cell carcinoma predicts survival and affects intratumoral CD8 T cells

Simeth, Jakob and Engelmann, Simon and Mayr, Roman and Kaelble, Sebastian and Weber, Florian and Pichler, Renate and Dettmer, Katja and Oefner, Peter J. and Hoering, Marcus and Symeou, Luisa and Freitag, Katharina and Wagner, Kilian and Burger, Maximilian and Herr, Wolfgang and Kreutz, Marina and Spang, Rainer and Liebisch, Gerhard and Siska, Peter J. (2025) Lipid metabolism of clear cell renal cell carcinoma predicts survival and affects intratumoral CD8 T cells. TRANSLATIONAL ONCOLOGY, 61: 102513. ISSN 1936-5233,

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Abstract

Background: Clear cell renal cell carcinoma (ccRCC), the most common RCC subtype, both accumulates and depletes selected lipid species. However, the prognostic role of lipid metabolic reprogramming in ccRCC has not been studied in detail so far. In addition, ccRCC can show a dense immune infiltration. Intriguingly, tumor infiltration with T cells can be negatively prognostic in RCC. This comprehensive study of the transcriptome, lipidome and immune infiltrate of ccRCC tumors elucidates the prognostic role of lipid-metabolic pathways and their possible interaction with tumor infiltrating T cells. Methods: Freshly resected RCC tumors and adjacent kidney tissues and extracellular fluids were processed and subjected to mass-spectrometry based lipidomics and lipid staining (n = 36). Hierarchical clustering was performed using the transcriptome data and gene group definitions obtained from publicly available databases (TCGA, 526 ccRCC and 287 papillary RCC). Phenotype, activation, proliferation and fatty acid uptake were assessed in ccRCC infiltrating T cells at single cell level ex vivo (n = 22) or after treatment with oleate and palmitate in vitro (n = 4). Results: ccRCC tumors accumulated lipids, notably those containing oleate. Clustering of RCC patients based on the expression of genes involved in fatty acid degradation (FAD) and cholesterol synthesis (chol) was able to predict survival and was superior to clustering based on genes involved in fatty acid synthesis or fatty acid elongation. Further, prognostic clustering was observed in ccRCC, but not in papillary RCC tumors, and it was independent of major clinical parameters. The FAD/chol cluster with poor prognosis showed a trend toward decreased prevalence of VHL mutations and higher c-MET copy numbers. Moreover, this cluster associated with dysregulated inflammation hallmarked by low PRF1, but high IFN gamma expression. Tumor infiltrating T cells showed increased fatty acid uptake, and CD8 T cell infiltration negatively correlated with oleate-associated lipid species found in the extracellular space of ccRCC tumors. Lastly, oleate treatment ex vivo suppressed the activation and perforin production of CD8 T cells from ccRCC tumors. Conclusions: Our study describes a robust, prognostic clustering of lipid gene expression that is both ccRCCspecific and independent of major parameters such as tumor size or aggressiveness. Furthermore, we propose that oleate accumulation in the RCC lipidome affects intratumoral CD8 T cell infiltration and function.

Item Type: Article
Uncontrolled Keywords: HIGH-THROUGHPUT QUANTIFICATION; COMPREHENSIVE MOLECULAR CHARACTERIZATION; DIFFERENTIAL EXPRESSION ANALYSIS; TUMOR-INFILTRATING LYMPHOCYTES; POOR-PROGNOSIS; BREAST-CANCER; MICROENVIRONMENT; CHOLESTEROL; SEQUENCES; ENZYMES; Lipids; Metabolism; Renal cell carcinoma; Oncoimmunology; T cell; Prognosis
Subjects: 600 Technology > 610 Medical sciences Medicine
Divisions: Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Medicine > Lehrstuhl für Hals-Nasen-Ohren-Heilkunde
Medicine > Lehrstuhl für Immunologie
Medicine > Lehrstuhl für Innere Medizin III (Hämatologie und Internistische Onkologie)
Medicine > Lehrstuhl für Klinische Chemie und Laboratoriumsmedizin
Medicine > Lehrstuhl für Pathologie
Medicine > Lehrstuhl für Urologie
Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Informatics and Data Science > Department Computational Life Science > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Depositing User: Dr. Gernot Deinzer
Date Deposited: 19 May 2026 07:13
Last Modified: 19 May 2026 07:13
URI: https://pred.uni-regensburg.de/id/eprint/65825

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