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An integrative approach based on GC–qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers

2021Journal article Open access
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dc.contributor.authorSilva, Catarina Luís
dc.contributor.authorPerestrelo, Rosa
dc.contributor.authorCapelinha, Filipa
dc.contributor.authorTomás, Helena
dc.contributor.authorCâmara, José S.
dc.date.accessioned2022-10-13T14:14:53Z
dc.date.available2022-10-13T14:14:53Z
dc.date.issued2021
dc.description.abstractIntroduction 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, C.L., Perestrelo, R., Capelinha, F. et al. An integrative approach based on GC–qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkers. Metabolomics 17, 72 (2021). https://doi.org/10.1007/s11306-021-01823-1pt_PT
dc.identifier.doi10.1007/s11306-021-01823-1pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/4703
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relationCentre of Statistics and its Applications
dc.relationNon-invasive, innovative and promising strategy for early diagnosis of breast cancer through the establishment of urinary volatile metabolomic profiles of breast cancer patients as well as from breast cancer tissues and breast cancer cell lines
dc.relationMadeira Chemistry Research Centre
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectBreast cancerpt_PT
dc.subjectTissuept_PT
dc.subjectUrinept_PT
dc.subjectNMRpt_PT
dc.subjectMSpt_PT
dc.subjectMetabolomicspt_PT
dc.subjectChemometric toolspt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.subjectCentro de Química da Madeira
dc.titleAn integrative approach based on GC–qMS and NMR metabolomics data as a comprehensive strategy to search potential breast cancer biomarkerspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre of Statistics and its Applications
oaire.awardTitleNon-invasive, innovative and promising strategy for early diagnosis of breast cancer through the establishment of urinary volatile metabolomic profiles of breast cancer patients as well as from breast cancer tissues and breast cancer cell lines
oaire.awardTitleMadeira Chemistry Research Centre
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FMAT%2F00006%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/OE/SFRH%2FBD%2F97039%2F2013/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FQUI%2F00674%2F2019/PT
oaire.citation.issue8pt_PT
oaire.citation.titleMetabolomicspt_PT
oaire.citation.volume17pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStreamOE
oaire.fundingStream6817 - DCRRNI ID
person.familyNameSousa Luís
person.familyNamePerestrelo
person.familyNameTomás
person.familyNameCâmara
person.givenNameCatarina Grace
person.givenNameRosa
person.givenNameHelena
person.givenNameJosé
person.identifierC-1300-2019
person.identifier1441319
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person.identifier.ciencia-id481C-08CE-90E5
person.identifier.orcid0000-0002-3018-3165
person.identifier.orcid0000-0002-7223-1022
person.identifier.orcid0000-0002-7856-2041
person.identifier.orcid0000-0003-1965-3151
person.identifier.ridI-2307-2014
person.identifier.ridE-5991-2010
person.identifier.scopus-author-id57194492726
person.identifier.scopus-author-id16686828800
person.identifier.scopus-author-id6508104177
person.identifier.scopus-author-id10140393000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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