LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes

Lun Jing, Jean-Marie Guigonis, Delphine Borchiellini, Matthieu Durand, Thierry Pourcher, Damien Ambrosetti, Lun Jing, Jean-Marie Guigonis, Delphine Borchiellini, Matthieu Durand, Thierry Pourcher, Damien Ambrosetti

Abstract

Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R2 = 75.0%, Q2 = 59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Renal cell carcinoma subtype classification based on untargeted metabolomics data. (a) OPLS-DA model of RCC subtype classification (N = 37). The model is composed of 2 predictive components and 1 orthogonal component and presents an R2X(cum) of 44.2%, a goodness-of-fit R2 of 75.0%, a goodness-of-prediction Q2 of 59.7% and a CV-ANOVA p-value of 7.516 × 10−8. (b) Validation plot obtained from 100 permutation tests. (c) ROC (Receiver Operating Characteristic) curves obtained from an independent cohort (N = 28) showing the ability of OPLS-DA model to predict RCC subtypes. (d) Loading plot showing the most discriminative metabolites. The metabolites with VIP (Variable Importance for the Projection) >3 are highlighted with red circles; with VIP >2 are highlighted with orange circles. Variables with VIP >3 are used for further pathway analysis.
Figure 2
Figure 2
Pathway analysis of altered metabolites in RCC subtypes. (a) Metabolite set enrichment analysis using SMPDB (Small Molecule Pathway Database). (b) Metabolomic pathway analysis using the KEGG database.
Figure 3
Figure 3
Example of metabolic profiling in tumour heterogeneity. (a) Macroscopic observation. (b) Metabolic profiling. Dendrogram of hierarchical clustering analysis (HCA). (c) Predicted scores of the 7 in-tumour heterogeneity samples in the OPLS-DA RCC subtype classification model. All 7 samples (black stars) were correctly predicted according to their subtype, ccRCC.
Figure 4
Figure 4
Main metabolic dysregulation among clear cell (cc), papillary (pap) and chromophobe (chro) RCC. Data were shown as the mean ± SEM. p-values were calculated using a Mann-Whitney test. The level of significance was set at *for p 

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