Investigation of urinary volatile organic metabolites as potential cancer biomarkers by solid-phase microextraction in combination with gas chromatography-mass spectrometry

BACKGROUND: Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer. METHODS: To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxenpolydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 501C for 60min using samples with high ionic strengths (17% sodium chloride, wv 1) and under agitation. RESULTS: A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (Po0.05). A significant increase in the peak area of 2-methyl3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients. CONCLUSIONS: Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

Dynamic headspace solid-phase microextraction combined with one-dimensional gas chromatography–mass spectrometry as a powerful tool to differentiate banana cultivars based on their volatile metabolite profile

In this study the effect of the cultivar on the volatile profile of five different banana varieties was evaluated and determined by dynamic headspace solid-phase microextraction (dHS-SPME) combined with one-dimensional gas chromatography–mass spectrometry (1D-GC–qMS). This approach allowed the definition of a volatile metabolite profile to each banana variety and can be used as pertinent criteria of differentiation. The investigated banana varieties (Dwarf Cavendish, Prata, Maçã, Ouro and Platano) have certified botanical origin and belong to the Musaceae family, the most common genomic group cultivated in Madeira Island (Portugal). The influence of dHS-SPME experimental factors, namely, fibre coating, extraction time and extraction temperature, on the equilibrium headspace analysis was investigated and optimised using univariate optimisation design. A total of 68 volatile organic metabolites (VOMs) were tentatively identified and used to profile the volatile composition in different banana cultivars, thus emphasising the sensitivity and applicability of SPME for establishment of the volatile metabolomic pattern of plant secondary metabolites. Ethyl esters were found to comprise the largest chemical class accounting 80.9%, 86.5%, 51.2%, 90.1% and 6.1% of total peak area for Dwarf Cavendish, Prata, Ouro, Maçã and Platano volatile fraction, respectively. Gas chromatographic peak areas were submitted to multivariate statistical analysis (principal component and stepwise linear discriminant analysis) in order to visualise clusters within samples and to detect the volatile metabolites able to differentiate banana cultivars. The application of the multivariate analysis on the VOMs data set resulted in predictive abilities of 90% as evaluated by the cross-validation procedure.

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosis

A sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography–mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

Proposta metodológica para o imageamento, caracterização, parametrização e geração de modelos virtuais de afloramentos

The aim of this work was to describe the methodological procedures that were mandatory to develop a 3D digital imaging of the external and internal geometry of the analogue outcrops from reservoirs and to build a Virtual Outcrop Model (VOM). The imaging process of the external geometry was acquired by using the Laser Scanner, the Geodesic GPS and the Total Station procedures. On the other hand, the imaging of the internal geometry was evaluated by GPR (Ground Penetrating Radar).The produced VOMs were adapted with much more detailed data with addition of the geological data and the gamma ray and permeability profiles. As a model for the use of the methodological procedures used on this work, the adapted VOM, two outcrops, located at the east part of the Parnaiba Basin, were selected. On the first one, rocks from the aeolian deposit of the Piaui Formation (Neo-carboniferous) and tidal flat deposits from the Pedra de Fogo Formation (Permian), which arises in a large outcrops located between Floriano and Teresina (Piauí), are present. The second area, located at the National Park of Sete Cidades, also at the Piauí, presents rocks from the Cabeças Formation deposited in fluvial-deltaic systems during the Late Devonian. From the data of the adapted VOMs it was possible to identify lines, surfaces and 3D geometry, and therefore, quantify the geometry of interest. Among the found parameterization values, a table containing the thickness and width, obtained in canal and lobes deposits at the outcrop Paredão and Biblioteca were the more relevant ones. In fact, this table can be used as an input for stochastic simulation of reservoirs. An example of the direct use of such table and their predicted radargrams was the identification of the bounding surface at the aeolian sites from the Piauí Formation. In spite of such radargrams supply only bi-dimensional data, the acquired lines followed of a mesh profile were used to add a third dimension to the imaging of the internal geometry. This phenomenon appears to be valid for all studied outcrops. As a conclusion, the tool here presented can became a new methodology in which the advantages of the digital imaging acquired from the Laser Scanner (precision, accuracy and speed of acquisition) were combined with the Total Station procedure (precision) using the classical digital photomosaic technique

Avaliação de nova estratégia extrativa, baseada em NTDs, combinada com dados de análise cromatigráfica e quimiométrica para a deteção de biomarcadores de diferentes tipos de cancro

A elevada incidência e mortalidade mundiais associadas ao cancro justificam o desenvolvimento e implementação de estratégias eficazes e não-invasivas conducentes a um diagnóstico precoce. Neste contexto, pretendeu-se avaliar a performance de uma metodologia inovadora, a microextração por “needle trap” (NTME), na extração de metabolitos voláteis (VOMs) da urina de pacientes oncológicos com diferentes tipos de cancro - cólon, pulmão e mama, e de indivíduos saudáveis, com a finalidade de identificar um conjunto de VOMs potenciais biomarcadores dos diferentes tipos cancros em estudo. De modo a maximizar a eficiência da extração dos VOMs, foram otimizados diferentes parâmetros experimentais, nomeadamente a natureza do sorvente, a temperatura, o tempo de equilíbrio, o volume de headspace, a força iónica, o pH do meio e o volume e a agitação da amostra. Usando como sorvente o DVB/Car1000/CarX, os melhores resultados foram obtidos com 4 mL de urina acidificada (pH= 2), 20% NaCl, 40 mL de headspace e 40 min de equilíbrio a 50 °C. Foi ainda avaliada a estabilidade dos VOMs no sorvente até 72 h após a extração. Nos quatro grupos em estudo foram identificados, por GC-MS, 259 VOMs pertencentes a diversas famílias químicas, nomeadamente cetonas, compostos sulfurados, furânicos e terpénicos. A matriz de dados obtida para cada grupo em estudo foi submetida a análise discriminante, usando o método dos mínimos quadrados parciais (PLS-DA), que resultou em clusters distintos diferenciadores de cada grupo. A aplicabilidade do modelo foi avaliada através do método de classificação SIMCA (modelagem suave e independente de analogias de classe), com elevadas taxas de classificação, sensibilidade e especificidade. Este foi o primeiro estudo usando NTME para o estabelecimento do padrão volatómico da urina. Os resultados obtidos revelam-se muito promissores originando perfis voláteis de maior expressividade, mais completos e abrangentes, que os obtidos usando metodologias de referência.

Avaliação de nova estratégia extrativa, baseada em NTDs, combinada com dados de análise cromatigráfica e quimiométrica para a deteção de biomarcadores de diferentes tipos de cancro

A elevada incidência e mortalidade mundiais associadas ao cancro justificam o desenvolvimento e implementação de estratégias eficazes e não-invasivas conducentes a um diagnóstico precoce. Neste contexto, pretendeu-se avaliar a performance de uma metodologia inovadora, a microextração por “needle trap” (NTME), na extração de metabolitos voláteis (VOMs) da urina de pacientes oncológicos com diferentes tipos de cancro - cólon, pulmão e mama, e de indivíduos saudáveis, com a finalidade de identificar um conjunto de VOMs potenciais biomarcadores dos diferentes tipos cancros em estudo. De modo a maximizar a eficiência da extração dos VOMs, foram otimizados diferentes parâmetros experimentais, nomeadamente a natureza do sorvente, a temperatura, o tempo de equilíbrio, o volume de headspace, a força iónica, o pH do meio e o volume e a agitação da amostra. Usando como sorvente o DVB/Car1000/CarX, os melhores resultados foram obtidos com 4 mL de urina acidificada (pH= 2), 20% NaCl, 40 mL de headspace e 40 min de equilíbrio a 50 °C. Foi ainda avaliada a estabilidade dos VOMs no sorvente até 72 h após a extração. Nos quatro grupos em estudo foram identificados, por GC-MS, 259 VOMs pertencentes a diversas famílias químicas, nomeadamente cetonas, compostos sulfurados, furânicos e terpénicos. A matriz de dados obtida para cada grupo em estudo foi submetida a análise discriminante, usando o método dos mínimos quadrados parciais (PLS-DA), que resultou em clusters distintos diferenciadores de cada grupo. A aplicabilidade do modelo foi avaliada através do método de classificação SIMCA (modelagem suave e independente de analogias de classe), com elevadas taxas de classificação, sensibilidade e especificidade. Este foi o primeiro estudo usando NTME para o estabelecimento do padrão volatómico da urina. Os resultados obtidos revelam-se muito promissores originando perfis voláteis de maior expressividade, mais completos e abrangentes, que os obtidos usando metodologias de referência.