Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

Abstract

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced.

Figure 1

Average ¹H NMR spectra of urine samples from (a) controls and (b) RCC patients. Legend: (1) isoleucine, (2) threonine, (3) lactate, (4) alanine, (5) acetate, (6) glutamate, (7) p-cresol sulfate (p-CS), (8) acetone, (9) valine, (10) citrate, (11) dimethylamine (DMA), (12) trimethylamine (TMA), (13) dimethylglycine (DMG), (14) creatine, (15) trimethylamine-N-oxide (TMAO), (16) 9-methyl-uric acid (tentative assignment), (17) methanol, (18) scyllo inositol, (19) glycine, (20) creatine, (21) trigonelline, (22) trigonellinamide, (23) unassigned (5.11 ppm), (24) glucose, (25) indoxyl sulfate (IS), (26) phenylacetylglutamine (PAG), (27) hippurate, (28) hypoxanthine, (29) formate. Arrows indicate visible spectral alterations. *Excluded regions (water and urea).

Figure 2

PLS-DA scores scatter plots obtained for the ¹H NMR spectra of urine of the unmatched cohort: n = 49 controls (○) vs. n = 39 RCC patients (●), (a) without and (b) with variable-selection; (c) age- and gender-matched sub-cohort: n = 28 controls (○) vs. n = 28 RCC patients (●), with variable-selection; (d) loadings plot corresponding to the model shown in (b). All models were obtained with no. LV = 2. The ellipses indicate the 95% confidence limits. The loadings plots are colored according to variable importance to the projection (VIP) and some assignments are indicated.

Figure 3

PCA scores scatter plots obtained for the NMR spectra of the urine of the unmatched cohort: n = 49 controls (○) vs. n = 39 RCC patients (●), (a) with the 32-resonance subset and (b) with the 23-metabolite signature, i.e. with bias resonances removed. All models were obtained with 2 principal components and the ellipses indicate the 95% confidence limits.

Figure 4. Schematic representation of possibly affected metabolic pathways in RCC.

Metabolites found significantly increased and decreased in the urine of RCC patients are presented in green and red, respectively. Metabolic steps hypothesized as up-regulated are presented by bold arrows and those that seem compromised as dashed arrows. 2-HBA: 2-hydroxy-butyrate acid;2-KG: 2-ketoglutarate; 2-Py: N-methyl-2-pyridone-5-carboxamide; AMP:adenosine monophosphate; GAA: guanidinoacetate; GMP: deoxy-guanosine; GSH: gluthatione; PAG:phenylacetylglutamine.