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- Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosisPublication . Silva, Catarina L.; Passos, Mário; Câmara, José S.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.
- Breast cancer metabolomics: from analytical platforms to multivariate data analysis. A reviewPublication . Silva, Catarina; Perestrelo, Rosa; Silva, Pedro; Tomás, Helena; Câmara, José S.Cancer is a major health issue worldwide for many years and has been increasing significantly. Among the different types of cancer, breast cancer (BC) remains the leading cause of cancer-related deaths in women being a disease caused by a combination of genetic and environmental factors. Nowadays, the available diagnostic tools have aided in the early detection of BC leading to the improvement of survival rates. However, better detection tools for diagnosis and disease monitoring are still required. In this sense, metabolomic NMR, LC-MS and GC-MS-based approaches have gained attention in this field constituting powerful tools for the identification of potential biomarkers in a variety of clinical fields. In this review we will present the current analytical platforms and their applications to identify metabolites with potential for BC biomarkers based on the main advantages and advances in metabolomics research. Additionally, chemometric methods used in metabolomics will be highlighted.
- An integrative approach based on GC–qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkersPublication . Silva, Catarina Luís; Perestrelo, Rosa; Capelinha, Filipa; Tomás, Helena; Câmara, José S.Introduction Globally, breast cancer (BC) is leading at the top of women's diseases and, as a multifactorial disease, there is the need for the development of new approaches to aid clinicians on monitoring BC treatments. In this sense, metabo lomic studies have become an essential tool allowing the establishment of interdependency among metabolites in biological samples. Objective The combination of nuclear magnetic resonance (NMR) and gas chromatography–quadrupole mass spectrometry (GC–qMS) based metabolomic analyses of urine and breast tissue samples from BC patients and cancer-free individuals was used. Methods Multivariate statistical tools were used in order to obtain a panel of metabolites that could discriminate malignant from healthy status assisting in the diagnostic feld. Urine samples (n=30), cancer tissues (n=30) were collected from BC patients, cancer-free tissues were resected outside the tumor margin from the same donors (n=30) while cancer-free urine samples (n=40) where obtained from healthy subjects and analysed by NMR and GC–qMS methodologies. Results The orthogonal partial least square discriminant analysis model showed a clear separation between BC patients and cancer-free subjects for both classes of samples. Specifcally, for urine samples, the goodness of ft (R2 Y) and predictive ability (Q2 ) was 0.946 and 0.910, respectively, whereas for tissue was 0.888 and 0.813, revealing a good predictable accuracy. The discrimination efciency and accuracy of tissue and urine metabolites was ascertained by receiver operating charac teristic curve analysis that allowed the identifcation of metabolites with high sensitivity and specifcity. The metabolomic pathway analysis identifed several dysregulated pathways in BC, including those related with lactate, valine, aspartate and glutamine metabolism. Additionally, correlations between urine and tissue metabolites were investigated and fve metabo lites (e.g. acetone, 3-hexanone, 4-heptanone, 2-methyl-5-(methylthio)-furan and acetate) were found to be signifcant using a dual platform approach. Conclusion Overall, this study suggests that an improved metabolic profle combining NMR and GC–qMS may be useful to achieve more insights regarding the mechanisms underlying cancer.
- Implementing a central composite design for the optimization of solid phase microextraction to establish the urinary volatomic expression: a first approach for breast cancerPublication . Silva, Catarina L.; Perestrelo, Rosa Maria de Sá; Silva, Pedro; Tomás, Helena; Câmara, JoséIntroduction Breast cancer (BC) is positioned as the second among all cancers remaining at the top of women´s diseases worldwide followed by colorectum, lung, cervix, and thyroid cancers. The main drawback of most the screening/diagnostic methods is their low sensitivity/specificity and in some cases the invasive procedure required to obtain the samples. Objectives On the present investigation, we report a statistical design was to evaluate by central composite design the influence towards the optimization of the most significant variables of solid-phase microextraction (SPME) procedure for the isolation of volatile organic metabolites (VOMs) from urine of BC patients (N = 31) and healthy individuals (CTL; N = 40). The establishment of the urinary volatomic composition, through gas chromatography-mass spectrometry (GC–MS) analysis, can boost the identification of volatile organic metabolites (VOMs) potential BC biomarkers useful to be used together or to complement the current BC diagnostics tools. Better early detection methods are needed to improve the outcomes of patients with BC. Methods Several combinations of experiments were considered with a central composite design (CCD) of response surface methodology (RSM) for the urinary volatomic pattern. Three-level three-factor CCD was employed assessing the most important extraction-influencing variables—fiber coating, NaCl amount, extraction time and temperature. The optimal conditions were achieved using a carboxen/polydimethylsiloxane fiber with 15% (w/v) NaCl during 75 min at 50 °C. Results A total of ten VOMs belonging to sulfur compounds, terpenoids and carbonyl compounds presented the highest contribution towards discrimination of BC patients from CTL (variable importance in projection (VIP) > 1, p < 0.05). The discrimination efficiency and accuracy of urinary metabolites was ascertained by receiver operating characteristic (ROC) curve analysis that allowed the identification of some metabolites with highest sensitivity and specificity to discriminate the groups. Conclusions The results obtained with this approach suggest the possibility to identify endogenous metabolites as a platform to discovery potential BC biomarkers and paves a way to explore the related metabolomic pathways in order to improve BC diagnostic tools.
- Volatomic pattern of breast cancer and cancer-free tissues as a powerful strategy to identify potential biomarkersPublication . Silva, Catarina; Perestrelo, Rosa; Silva, Pedro; Capelinha, Filipa; Tomás, Helena; Câmara, José S.Breast cancer (BC), ranked as the fifth amongst all cancers, remains at the top of women’s cancers worldwide followed by colorectal, lung, cervix, and stomach cancers. The main handicap of most of the screening/diagnostic methods is based on their low sensitivity and specificity and the invasive behavior of most sampling procedures. The aim of this study was to establish the volatomic pattern of BC and cancer-free (CF) tissues (n = 30) from the same patients, as a powerful tool to identify a set of volatile organic metabolite (VOM) potential BC biomarkers which might be used together or complement with the traditional BC diagnostics strategies, through the integration of chromatographic data, obtained by solid-phase microextraction followed by gas chromatography-mass spectrometry (SPME/GC-qMS), with chemometric tools. A total of four metabolites: limonene, decanoic acid, acetic acid and furfural presented the highest contribution towards discrimination of BC and CF tissues (VIP > 1, p < 0.05). The discrimination efficiency and accuracy of BC tissue metabolites was ascertained by ROC curve analysis that allowed the identification of some metabolites with high sensitivity and specificity. The results obtained with this approach suggest the possibility of identifying endogenous metabolites as a platform to find potential BC biomarkers and pave the way to investigate the related metabolomic pathways in order to improve BC diagnostic tools. Moreover, deeper investigations could unravel novel mechanistic insights into the disease pathophysiology.
- Lipid biosignature of breast cancer tissues by matrix-assisted laser desorption/ionization time-of-flight mass spectrometryPublication . Silva, Catarina L.; Perestrelo, Rosa; Sousa-Ferreira, Ivo; Capelinha, Filipa; Câmara, José S.; Petković, MarijanaPurpose One of the hallmarks of cancer cells is the demand of supply for the synthesis of new membranes involved in cell proliferation and lipids have an important role in cellular structure, signaling pathways and progression of cancer. In this sense, lipid studies have become an essential tool allowing the establishment of signatures associated with breast cancer (BC). In this regard, some metabolic processes including proteins, nucleic acids and lipid synthesis are enhanced as part of cancer-associated metabolic reprogramming, as a requirement for cell growth and proliferation. Methods Pairwise samples of breast active carcinoma (BAC) and breast cancer-free tissues were collected from n=28 patients and analyzed by MALDI-TOF MS. Results Major lipid species are identifed in the MALDI-TOF mass spectra, with certain phosphatidylinositols (PIs) detect able only in BAC. Statistical analysis revealed signifcant diferences (p<0.05) between ratios lysophosphatidylcholine (LPC) 16:0/phosphatidylcholine (PC) 16:0_18:2 between AC and CF groups as well as for BC stages II and III. The ratio PC 16:0_18:2/PC16:0_18:1 was statistically diferent between AC and CF groups. The one-way ANOVA revealed that there are no statistical diferences among BC stages (I, II and III) within AC group. Comparing BC stages, the signifcance impact increased (p<0.05) with stage. Conclusion The obtained data revealed MALDI-TOF MS as a powerful tool to explore lipid signatures and the enzyme activity associated with BC and possibly establish novel disease markers.
- Volatile metabolomic signature of human breast cancer cell linesPublication . Silva, Catarina L.; Perestrelo, Rosa; Silva, Pedro; Tomás, Helena; Câmara, José S.Breast cancer (BC) remains the most prevalent oncologic pathology in women, causing huge psychological, economic and social impacts on our society. Currently, the available diagnostic tools have limited sensitivity and specificity. Metabolome analysis has emerged as a powerful tool for obtaining information about the biological processes that occur in organisms, and is a useful platform for discovering new biomarkers or make disease diagnosis using different biofluids. Volatile organic compounds (VOCs) from the headspace of cultured BC cells and normal human mammary epithelial cells, were collected by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography combined with mass spectrometry (GC-MS), thus defining a volatile metabolomic signature. 2-Pentanone, 2-heptanone, 3-methyl-3-buten-1-ol, ethyl acetate, ethyl propanoate and 2-methyl butanoate were detected only in cultured BC cell lines. Multivariate statistical methods were used to verify the volatomic differences between BC cell lines and normal cells in order to find a set of specific VOCs that could be associated with BC, providing comprehensive insight into VOCs as potential cancer biomarkers. The establishment of the volatile fingerprint of BC cell lines presents a powerful approach to find endogenous VOCs that could be used to improve the BC diagnostic tools and explore the associated metabolomic pathways.
- Untargeted urinary 1H NMR-based metabolomic pattern as a potential platform in breast cancer detectionPublication . Silva, Catarina L.; Olival, Ana; Perestrelo, Rosa; Silva, Pedro; Tomás, Helena; Câmara, José S.: Breast cancer (BC) remains the second leading cause of death among women worldwide. An emerging approach based on the identification of endogenous metabolites (EMs) and the establishment of the metabolomic fingerprint of biological fluids constitutes a new frontier in medical diagnostics and a promising strategy to differentiate cancer patients from healthy individuals. In this work we aimed to establish the urinary metabolomic patterns from 40 BC patients and 38 healthy controls (CTL) using proton nuclear magnetic resonance spectroscopy (1H-NMR) as a powerful approach to identify a set of BC-specific metabolites which might be employed in the diagnosis of BC. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to a 1H-NMR processed data matrix. Metabolomic patterns distinguished BC from CTL urine samples, suggesting a unique metabolite profile for each investigated group. A total of 10 metabolites exhibited the highest contribution towards discriminating BC patients from healthy controls (variable importance in projection (VIP) >1, p < 0.05). The discrimination efficiency and accuracy of the urinary EMs were ascertained by receiver operating characteristic curve (ROC) analysis that allowed the identification of some metabolites with the highest sensitivities and specificities to discriminate BC patients from healthy controls (e.g. creatine, glycine, trimethylamine N-oxide, and serine). The metabolomic pathway analysis indicated several metabolism pathway disruptions, including amino acid and carbohydrate metabolisms, in BC patients, namely, glycine and butanoate metabolisms. The obtained results support the high throughput potential of NMR-based urinary metabolomics patterns in discriminating BC patients from CTL. Further investigations could unravel novel mechanistic insights into disease pathophysiology, monitor disease recurrence, and predict patient response towards therapy.
- Non-invasive, innovative and promising strategy for breast cancer diagnosis based on metabolomic profile of urine, cancer cell lines and tissuePublication . Silva, Catarina Grace Sousa Luís; Câmara, José de Sousa; Tomás, Helena Maria Pires GasparThe work presented in this thesis aimed to establish the metabolomic profile of urine and breast cancer (BC) tissue from BC patients (samples cordially provided by Funchal Hospital), in addition to BC cell lines (MCF-7, MDA-MB-231, T-47D) as a powerful strategy to identify metabolites as potential BC biomarkers, helping on the development of non-invasive approaches for BC diagnosis and management. To achieve the main goal and obtain a deeper and comprehensive knowledge on BC metabolome, different analytical platforms, namely headspace solid-phase microextraction (HSSPME) combined with gas chromatography-quadrupole mass spectrometry (GC-qMS) and nuclear magnetic ressonance (1H NMR) spectroscopy were used. The application of multivariate statistical methods - principal component analysis (PCA) and orthogonal partial least square – discriminant analysis (OPLS-DA), to data matrix obtained from the different target samples allowed to find a set of highly sensitive and specific metabolites metabolites, namely, 4-heptanone, acetic acid and glutamine, able to be used as potential biomarkers in BC diagnosis. Significant group separation was observed in OPLS-DA score plot between BC and CTL indicating intrinsic metabolic alterations in each group. To attest the robustness of the model, a random permutation test with 1000 permutations was performed with OPLS-DA. The permutation test yielded R2 (represents goodness of fit) and Q2 values (represents predictive ability) with values higher than 0.717 and 0.691, respectively. Several metabolic pathways were dysregulated in BC considering the analytical approaches used. The main pathways included pyruvate, glutamine and sulfur metabolisms, indicating that there might be an association between the metabolites arising from the type of biological sample of the same donor used to perform the investigation. The integration of data obtained from different analytical platforms (GC-qMS and 1H NMR) for urinary and tissue samples revealed that five metabolites (e.g., acetone, 3-hexanone, 4-heptanone, 2methyl-5-(methylthio)-furan and acetate), were found significant using a dual analytical approach.