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A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
Published on: April 12, 2017
Metabolic Reprogramming in Renal Cell Carcinoma: A Scoping Review with Quantitative Integration of Metabolomic
Filipa Amaro1,2, Márcia Carvalho1,2,3,4, Maria de Lourdes Bastos1,2
1Associate Laboratory i4HB - Institute for Health and Bioeconomy, University of Porto, 4050-313 Porto, Portugal.
None:
A deeper understanding of renal cell carcinoma (RCC)-specific molecular alterations is crucial for improving diagnosis, predicting outcomes, and advancing therapeutic development. Metabolomics enables systematic profiling of small molecules and provides direct functional readouts of tumor biology, offering a powerful route to characterize metabolic reprogramming and identify clinically actionable biomarkers. We conducted a scoping review of 45 RCC metabolomic studies using tissue, urine, blood, and cell line models and performed a quantitative synthesis specific to each matrix using the Amanida package, focusing on the metabolites reported most consistently across the studies. Tissue analysis demonstrated consistent metabolic reprogramming in metabolites related to the core energy and amino acid pathways. Urine studies identified significantly altered metabolites with potential utility as noninvasive biomarkers. Blood-based and in vitro data sets were too limited and heterogeneous to support quantitative synthesis. Across studies, consistent dysregulation in metabolites associated with glutathione, lipid, inositol phosphate, and purine/pyrimidine pathways highlights underexplored metabolic processes with potential diagnostic and therapeutic relevance. Taken together, the data indicate consistent tissue metabolic signatures reflecting RCC biology and identify urinary metabolites as strong candidates for biomarker validation. Standardized analytical and reporting frameworks, coupled with validation in larger, well-characterized cohorts, are needed to accelerate clinical implementation of RCC metabolomic biomarkers and metabolism-informed targets.
