Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Metabolic signature of renal cell carcinoma tumours and its correlation with the urinary metabolome

INTRODUCTION

Despite considerable advances in cancer research, the increasing prevalence and high mortality rate of clear cell renal cell carcinoma (ccRCC) remain a significant challenge. A more detailed comprehension of the distinctive metabolic characteristics of ccRCC is vital to enhance diagnostic, prognostic, and therapeutic strategies.

OBJECTIVES

This study aimed to investigate the metabolic signatures of ccRCC tumours and, for the first time, their correlation with the urinary metabolome of the same patients.

METHODS

We applied a gas chromatography-mass spectrometry (GC-MS)-based metabolomic approach to analyse matched tissue and urine samples from a cohort of 18 ccRCC patients and urine samples from 18 cancer-free controls. Multivariate and univariate statistical methods, as well as pathway and correlation analyses, were performed to assess metabolic dysregulations and correlations between tissue and urine.

RESULTS

The results showed a ccRCC metabolic signature characterized by reprogramming in amino acid, energy, and sugar and inositol phosphate metabolisms. Our study identified, for the first time, significantly decreased levels of asparagine, proline, gluconate, 3-aminoisobutanoate, 4-aminobutanoate and urea in ccRCC tumours, highlighting the involvement of arginine biosynthesis, β-alanine metabolism and purine and pyrimidine metabolism in ccRCC. The correlations between tissue and urine metabolomes provide evidence for the potential usefulness of urinary metabolites in understanding systemic metabolic changes driven by RCC tumours.

CONCLUSIONS

These findings significantly advance our understanding of metabolic reprogramming in ccRCC and the systemic metabolic changes associated with the disease. Future research is needed to validate these findings in larger cohorts and to determine their potential implications for diagnosis and targeted therapies.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.