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.

Breath volatile organic compound (VOC) analysis can open a non-invasive window onto pathological and metabolic processes in the body. Decades of clinical breath-gas analysis have revealed that changes in exhaled VOC concentrations are important rather than disease specific biomarkers. As physiological parameters, such as respiratory rate or cardiac output, have profound effects on exhaled VOCs, here we investigated VOC exhalation under respiratory manoeuvres. Breath VOCs were monitored by means of real-time mass-spectrometry during conventional FEV manoeuvres in 50 healthy humans. Simultaneously, we measured respiratory and hemodynamic parameters noninvasively. Tidal volume and minute ventilation increased by 292 and 171% during the manoeuvre. FEV manoeuvre induced substance specific changes in VOC concentrations. pET-CO2 and alveolar isoprene increased by 6 and 21% during maximum exhalation. Then they decreased by 18 and 37% at forced expiration mirroring cardiac output. Acetone concentrations rose by 4.5% despite increasing minute ventilation. Blood-borne furan and dimethyl-sulphide mimicked isoprene profile. Exogenous acetonitrile, sulphides, and most aliphatic and aromatic VOCs changed minimally. Reliable breath tests must avoid forced breathing. As isoprene exhalations mirrored FEV performances, endogenous VOCs might assure quality of lung function tests. Analysis of exhaled VOC concentrations can provide additional information on physiology of respiration and gas exchange.

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.

Invasive ductal carcinoma (IDC) is the most common type of breast cancer and the leading cause of breast cancer related mortality. In the present study, metabolomic profiles of 72 tissue samples and 146 serum samples were analysed using targeted liquid chromatography multiple reaction monitoring mass spectrometry (LC-MRM/MS) and untargeted gas chromatography mass spectrometry (GC-MS) approaches. Combination of univariate and multivariate statistical treatment identified significant alterations of 42 and 32 metabolites in tissue and serum samples of IDC, respectively when compared to control. Some of the metabolite changes from tissue were also reflected in serum, indicating a bi-directional interaction of metabolites in IDC. Additionally, 8 tissue metabolites and 9 serum metabolites showed progressive change from control to benign to IDC suggesting their possible role in malignant transformation. We have identified a panel of three metabolites viz. tryptophan, tyrosine, and creatine in tissue and serum, which could be useful in screening of IDC subjects from both control and benign. The metabolomic alterations in IDC showed perturbations in purine and pyrimidine metabolism, amino sugar metabolism, amino acid metabolism, fatty acid biosynthesis etc. Comprehensively, this study provides valuable insights into metabolic adaptations of IDC, which can help to identify diagnostic markers as well as potential therapeutic targets.

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Methods Volatomic analysis of urine samples collected from HNC patients (n = 29) and healthy controls (n = 31) was performed using headspace solid phase microextraction coupled to gas chromatography mass spectrometry (GC-MS). Both univariate and multivariate statistical approaches were used to investigate HNC specific volatomic alterations. Results Statistical analysis revealed a total of 28 metabolites with highest contribution towards discrimination of HNC patients from healthy controls (VIP >1, p < 0.05, Log 2 FC ≥0.58/≤−0.57). The discrimination efficiency and accuracy of urinary VOCs was ascertained by ROC curve analysis that allowed the identification of four metabolites viz. 2,6-dimethyl-7-octen-2-ol, 1-butanol, p-xylene and 4-methyl-2-heptanone with highest sensitivity and specificity to discriminate HNC patients from healthy controls. Further, the metabolic pathway analysis identified several dysregulated pathways in HNC patients and their detailed investigations could unravel novel mechanistic insights into the disease pathophysiology. Conclusion Overall, this study provides valuable fingerprint of the volatile profile of HNC patients, which in turn, might help in improving the current understanding of this form of cancer and lead to the development of non-invasive approaches for HNC diagnosis.

Globally, breast cancer is the second most common cancer among women. Although biomarker discoveries through various proteomic approaches of tissue and serum samples have been studied in breast cancer, urinary proteome alterations in breast cancer are least studied. Urine being a noninvasive biofluid and a significant source of proteins, it has the potential in early diagnosis of breast cancer. This study used complementary quantitative gel-based and gel-free proteomic approaches to find a panel of urinary protein markers that could discriminate HER2 enriched (HE) subtype breast cancer from the healthy controls. A total of 183 differentially expressed proteins were identified using three complementary approaches, namely 2D-DIGE, iTRAQ, and sequential window acquisition of all theoretical mass spectra. The differentially expressed proteins were subjected to various bioinformatics analyses for deciphering the biological context of these proteins using protein analysis through evolutionary relationships, database for annotation, visualization and integrated discovery, and STRING. Multivariate statistical analysis was undertaken to identify the set of most significant proteins, which could discriminate HE breast cancer from healthy controls. Immunoblotting and MRM-based validation in a separate cohort testified a panel of 21 proteins such as zinc-alpha2-glycoprotein, A2GL, retinol-binding protein 4, annexin A1, SAP3, SRC8, gelsolin, kininogen 1, CO9, clusterin, ceruloplasmin, and α1-antitrypsin could be a panel of candidate markers that could discriminate HE breast cancer from healthy controls.