ArticleJournal of translational medicine2023
Metabolic classifications of renal cell carcinoma reveal intrinsic connections with clinical and immune characteristics.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- ZBED6-IGF2-PIK3C3 autophagy axis drives ccRCC progression: A multi-omics integration study.iScience · 2026Article
- Lipid metabolism classification of gliomas.Scientific reports · 2026Article
- Biomarker-Based Precision Prediction of Immunotherapy Response in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2025Article
- The metabolic crossroads: glycolysis in cancer and neurodegeneration.BMC neurology · 2025Article
- NKX2-1 Restricts the Growth and Metastasis of Lung Squamous Cell Carcinoma Through Transcriptive Suppression of AKR1B10.Journal of biochemical and molecular toxicology · 2025Article
- K-Means Clustering of HyperpolarisedCancers · 2025Article
- Prediction of Prognosis and Immunotherapy Response of Gastric Cancer Based on Glutamine Metabolism-related Genes.Current medicinal chemistry · 2025Article
- Article
- Reprogramming of lipid metabolism in the tumor microenvironment: a strategy for tumor immunotherapy.Lipids in health and disease · 2024Review
- Angiopoietin-like protein 4 dysregulation in kidney diseases: a promising biomarker and therapeutic target.Frontiers in pharmacology · 2024Review
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKidney cancer undergoes a dramatic metabolic shift and has demonstrated responsiveness to immunotherapeutic intervention. However, metabolic classification and the associations between metabolic alterations and immune infiltration in Renal cell carcinoma still remain elucidative.
methodsUnsupervised consensus clustering was conducted on the TCGA cohorts for metabolic classification. GESA, mRNAsi, prognosis, clinical features, mutation load, immune infiltration and differentially expressed gene differences among different clusters were compared. The prognosis model and nomograms were constructed based on metabolic gene signatures and verified using external ICGC datasets. Immunohistochemical results from Human Protein Atlas database and Tongji hospital were used to validate gene expression levels in normal tissues and tumor samples. CCK8, apoptosis analysis, qPCR, subcutaneously implanted murine models and flowcytometry analysis were applied to investigate the roles of ACAA2 in tumor progression and anti-tumor immunity.
resultsRenal cell carcinoma was classified into 3 metabolic subclusters and the subcluster with low metabolic profiles displayed the poorest prognosis, highest invasiveness and AJCC grade, enhanced immune infiltration but suppressive immunophenotypes. ACAA2, ACAT1, ASRGL1, AKR1B10, ABCC2, ANGPTL4 were identified to construct the 6 gene-signature prognosis model and verified both internally and externally with ICGC cohorts. ACAA2 was demonstrated as a tumor suppressor and was associated with higher immune infiltration and elevated PD-1 expression of CD8
conclusionsOur research proposed a new metabolic classification method for RCC and revealed intrinsic associations between metabolic phenotypes and immune profiles. The identified gene signatures might serve as key factors bridging tumor metabolism and tumor immunity and warrant further in-depth investigations.
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