Screening of salivary volatiles for putative breast cancer discrimination: an exploratory study involving geographically distant populations.

Saliva is possibly the easiest biofluid to analyse and, despite its simple composition, contains relevant metabolic information. In this work, we explored the potential of the volatile composition of saliva samples as biosignatures for breast cancer (BC) non-invasive diagnosis. To achieve this, 106 saliva samples of BC patients and controls in two distinct geographic regions in Portugal and India were extracted and analysed using optimised headspace solid-phase microextraction gas chromatography mass spectrometry (HS-SPME/GC-MS, 2 mL acidified saliva containing 10% NaCl, stirred (800 rpm) for 45 min at 38 °C and using the CAR/PDMS SPME fibre) followed by multivariate statistical analysis (MVSA). Over 120 volatiles from distinct chemical classes, with significant variations among the groups, were identified. MVSA retrieved a limited number of volatiles, viz. 3-methyl-pentanoic acid, 4-methyl-pentanoic acid, phenol and p-tert-butyl-phenol (Portuguese samples) and acetic, propanoic, benzoic acids, 1,2-decanediol, 2-decanone, and decanal (Indian samples), statistically relevant for the discrimination of BC patients in the populations analysed. This work defines an experimental layout, HS-SPME/GC-MS followed by MVSA, suitable to characterise volatile fingerprints for saliva as putative biosignatures for BC non-invasive diagnosis. Here, it was applied to BC samples from geographically distant populations and good disease separation was obtained. Further studies using larger cohorts are therefore very pertinent to challenge and strengthen this proof-of-concept study. Graphical abstract ᅟ.

Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview.

Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

Microextraction by Packed Sorbent (MEPS) and Solid-Phase Microextraction (SPME) as Sample Preparation Procedures for the Metabolomic Profiling of Urine.

For a long time, sample preparation was unrecognized as a critical issue in the analytical methodology, thus limiting the performance that could be achieved. However, the improvement of microextraction techniques, particularly microextraction by packed sorbent (MEPS) and solid-phase microextraction (SPME), completely modified this scenario by introducing unprecedented control over this process. Urine is a biological fluid that is very interesting for metabolomics studies, allowing human health and disease characterization in a minimally invasive form. In this manuscript, we will critically review the most relevant and promising works in this field, highlighting how the metabolomic profiling of urine can be an extremely valuable tool for the early diagnosis of highly prevalent diseases, such as cardiovascular, oncologic and neurodegenerative ones.