Biomarkers in renal cell carcinoma: a metabolomics approach

Since Otto Warburg, many studies have explored the unique metabolic phenotype of cancer cells highlighting the value and applicability of metabolomics in the oncology field, particularly in the development of cancer biomarkers. With respect to renal cell carcinoma (RCC), a metabolomics approach would own a great potential since urinary system is intimately connected with urine and, this biofluid, offers some advantages allowing the development of an assay suitable for use in clinical practice. Moreover, the assessment of metabolic derangements characteristics of RCC might provide a complete health assessment of this pathology, enabling the development of novel targeted therapies and even the stratification of responsive patients to specific therapeutic options improving the effectiveness of therapy. Metabolomic studies performed so far showed that the RCC metabolic signature is characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid catabolism, known to be crucial to cell proliferation. Despite some of those alterations are common to carcinogenesis, the potential role of acylcarnitines, gentisate, α-ketoglutarate and quinolinate in RCC pathophysiology has been proposed recently. The ability of metabolomics to discriminate between RCC and normal samples shows convincing evidence of its applicability in RCC management. Furthermore, the studies already carried out have not only tried to unveil the metabolic profile of RCC but also to evaluate the impact of some factors, namely technical, in RCC-metabolomics research. This type of study is pivotal in the design of metabolomics studies, helping to increase the reliability of the results. The present review updates the current knowledge on the metabolic alterations recognized in the RCC, and provides insight to the characteristics, strengths, limitations, and recent advances in RCC-metabolomics studies, always keeping in mind its potential application in clinical/health areas as a biomarker discovery tool.