Loading...
6 results
Search Results
Now showing 1 - 6 of 6
- 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.
- 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.