INTRODUCTION

Kidney cancer (KC) is a significant global health burden. Early diagnosis remains challenging due to the limited sensitivity and specificity of existing biomarkers. Metabolomics enables the detection of disease-specific metabolic alterations, offering potential for improved non-invasive biomarker discovery.

OBJECTIVES

This study aims to characterize metabolic signatures distinguishing KC patients from non-cancer controls and evaluate the diagnostic potential of annotated metabolites in serum and urine.

METHODS

An untargeted metabolomic analysis was performed on serum and urine samples from 56 KC patients and 200 controls using ultra-high-resolution mass spectrometry coupled with ultra-high-performance liquid chromatography (UHPLC-UHRMS in both positive and negative ionization modes with vacuum insulated probe heated electrospray ionization (VIP-HESI)). Samples were collected from the same individuals, which helped minimize inter-individual variability and enabled cross-biofluid comparison of metabolic profiles. Multivariate statistical techniques were applied to detect metabolic differences, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). An external validation strategy using training and validation subsets was employed to assess the robustness of candidate metabolite biomarkers matched in the discovery dataset.

RESULTS

Distinct metabolic signatures were observed between KC patients and controls, with key metabolic pathways involving lipid metabolism, amino acid biosynthesis, and glycerophospholipid metabolism. 19 serum and 12 urine metabolites showed high diagnostic potential (AUC > 0.90), demonstrating strong sensitivity and specificity.

CONCLUSION

These findings support the application of metabolomics for RCC detection and highlight the metabolic alterations associated with kidney cancer. Further validation in larger cohorts is necessary to confirm the clinical utility of these potential biomarkers.