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.

Insights

This review of renal cell carcinoma (RCC) metabolomics reveals consistent tissue metabolic signatures and identifies urinary metabolites as promising noninvasive biomarkers for improved diagnosis and treatment strategies.

Area of Science:

  • Oncology
  • Metabolomics
  • Biochemistry

Background:

  • Understanding molecular alterations in renal cell carcinoma (RCC) is vital for improving patient outcomes.
  • Metabolomics offers a functional readout of tumor biology by profiling small molecules.
  • Metabolic reprogramming is a hallmark of cancer, including RCC.

Purpose of the Study:

  • To systematically review and synthesize findings from RCC metabolomic studies.
  • To identify consistent metabolic alterations across different sample types (tissue, urine, blood, cell lines).
  • To pinpoint potential biomarkers for RCC diagnosis and therapeutic development.

Main Methods:

  • Conducted a scoping review of 45 RCC metabolomic studies.
  • Performed quantitative synthesis of metabolites across different matrices (tissue, urine, blood, cell lines) using the Amanida package.
  • Focused on metabolites consistently reported across multiple studies.

Main Results:

  • Tissue analysis revealed consistent metabolic reprogramming in energy and amino acid pathways.
  • Urine studies identified significantly altered metabolites with potential as noninvasive biomarkers.
  • Consistent dysregulation observed in glutathione, lipid, inositol phosphate, and purine/pyrimidine pathways across studies.

Conclusions:

  • Consistent tissue metabolic signatures reflect underlying RCC biology.
  • Urinary metabolites show strong potential for biomarker validation in noninvasive diagnostics.
  • Standardized frameworks and larger cohort validation are crucial for clinical implementation of RCC metabolomic biomarkers.