Untargeted metabolomic profiling of serum and urine in kidney cancer: a non-invasive approach for biomarker discovery

Abstract

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.

Keywords: Biomarkers; Kidney cancer; Metabolomics; Serum; UHPLC-UHRMS; Urine.

Fig. 1

Metabolomic analysis of serum samples from KC and NC groups in the training set. A PCA and B OPLS-DA score plots distinguishing tumor (violet) and control (orange) serum samples. C Receiver operating characteristic (ROC) curves illustrating the diagnostic performance. D–G Box-and-whisker plots of selected metabolites differentiating KC and control samples. Y-axis values represent MS signal intensities after log₁₀ transformation and autoscaling (z-score normalization). ***P value < 0.001. PC principal component, AUC area under the curve, CI confidence interval

Fig. 2

Metabolomic analysis of urine samples from KC and NC groups in the training set. A PCA and B OPLS-DA score plots distinguishing tumor (violet) and control (orange) urine samples. C Receiver operating characteristic (ROC) curves illustrating the diagnostic performance. D–G Box-and-whisker plots of selected metabolites differentiating KC and control samples. Y-axis values represent MS signal intensities after log₁₀ transformation and autoscaling (z-score normalization). ***P value < 0.001. PC principal component, AUC area under the curve, CI confidence interval

Fig. 3

Pathway and chemical class analysis of differentiating metabolites in KC. A, B Pathway analysis of statistically significant metabolites in serum (A) and urine (B), with bubble size indicating pathway impact and color representing significance (red = highest, white = lowest). Numbered pathways correspond to the most enriched results: in serum (A) 1—linoleic acid metabolism; 2—valine, leucine and isoleucine biosynthesis, 3—biosynthesis of unsaturated fatty acids 4—glycerophospholipid metabolism; 5—propanoate metabolism; 6—alanine, aspartate and glutamate metabolism; 7—alpha-linolenic acid metabolism; 8—glycine, serine and threonine metabolism; and in urine (B) 1—linoleic acid metabolism; 2—glycerophospholipid metabolism; 3—tyrosine metabolism; 4—sphingolipid metabolism; 5—histidine metabolism (C, D) Distribution of significantly altered metabolites in serum and urine samples, categorized by pathway (C) and main-chemical subclass (D). In C, bars indicate the number of upregulated (dark green = serum, brown = urine) and downregulated (light green = serum, light brown = urine) metabolites in KC samples, mapped to each enriched KEGG or SMPDB pathway. In panel D, metabolites are grouped by chemical subclass, with color intensity indicating direction and sample origin (dark blue = serum upregulated, gray = urine upregulated; light blue = serum downregulated, light gray = urine downregulated). The x-axis in both panels shows the number of differential metabolites per category