Over the last decades there has been a change in biomedical research with the search for single genes, transcripts, proteins, or metabolites being substituted by the coverage of the entire genome, transcriptome, proteome, and metabolome with the "omics" approaches. The emergence of metabolomics, defined as the comprehensive analysis of all metabolites in a system, is still recent compared to other "omics" fields, but its particular features and the improvement of both analytical techniques and pattern recognition methods has contributed greatly to its increasingly use. The feasibility of metabolomics for biomarker discovery is supported by the assumption that metabolites are important players in biological systems and that diseases cause disruption of biochemical pathways, which are not new concepts. In fact, metabolomics, meaning the parallel assessment of multiple metabolites, has been shown to have benefits in various clinical areas. Compared to classical diagnostic approaches and conventional clinical biomarkers, metabolomics offers potential advantages in sensitivity and specificity. Despite its potential, metabolomics still retains several intrinsic limitations which have a great impact on its widespread implementation - these limitations in biological and experimental measurements. This review will provide an insight to the characteristics, strengths, limitations, and recent advances in metabolomics, always keeping in mind its potential application in clinical/ health areas as a biomarker discovery tool.

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The analysis of volatile organic compounds (VOCs) emanating from biological samples appears as one of the most promising approaches in metabolomics for the study of diseases, namely cancer. In fact, it offers advantages, such as non‐invasiveness and robustness for high‐throughput applications. The purpose of this work was to study the urinary volatile metabolic profile of patients with renal cell carcinoma (RCC) (n = 30) and controls (n = 37) with the aim of identifying a potential specific urinary volatile pattern as a non‐invasive strategy to detect RCC. Moreover, the effect of some confounding factors such as age, gender, smoking habits and body mass index was evaluated as well as the ability of urinary VOCs to discriminate RCC subtypes and stages. A headspace solid‐phase microextraction/gas chromatography–mass spectrometry‐based method was performed, followed by multivariate data analysis. A variable selection method was applied to reduce the impact of potential redundant and noisy chromatographic variables, and all models were validated by Monte Carlo cross‐validation and permutation tests. Regarding the effect of RCC on the urine VOCs composition, a panel of 21 VOCs descriptive of RCC was defined, capable of discriminating RCC patients from controls in principal component analysis. Discriminant VOCs were further individually validated in two independent samples sets (nine RCC patients and 12 controls, seven RCC patients with diabetes mellitus type 2) by univariate statistical analysis. Two VOCs were found consistently and significantly altered between RCC and controls (2‐oxopropanal and, according to identification using NIST14, 2,5,8‐trimethyl‐1,2,3,4‐tetrahydronaphthalene‐1‐ol), strongly suggesting enhanced potential as RCC biomarkers. Gender, smoking habits and body mass index showed negligible and age‐only minimal effects on the urinary VOCs, compared to the deviations resultant from the disease. Moreover, in this cohort, the urinary volatilome did not show ability to discriminate RCC stages and histological subtypes. The results validated the value of urinary volatilome for the detection of RCC and advanced with the identification of potential RCC urinary biomarkers.