Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFG |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/12452 |
Resumo: | Oral squamous cell carcinoma (OSCC) is the most common oral cavity cancer, responsible for 90% of all cancers in the head-neck region, except for non-melanoma skin cancer. A fast, accurate, and cheap diagnosis is required to detect the presence of OSCC in the preliminary stages providing better chances of success in cancer treatment. In general, the diagnosis is performed using proteomics and histological data. However, lipids play a key role in cellular metabolism. Therefore, understanding the lipid differentiation between healthy and cancer tissues could be important to predict biomarkers candidates and improve the diagnosis system. Furthermore, a new methodology for simultaneous RNA and lipid extraction using TRIzol® solution was developed, and the lipid profile in both sample groups was studied. The samples stored in TRIzol® solution were homogenized and submitted to liquid-liquid microextraction (LLME) using chloroform as an extractive solvent. Therefore, the organic phase was collected and submitted to the Bligh &Dyer extraction. The simultaneous RNA and lipid extraction was validated according to the parameters described by the Brazilian Nation Health Surveillance Agency. An analytical curve of tissue in methanol and another one of tissue in TRIzol® solution were performed for method evaluation. The sample solutions were spiked with different concentrations of PC 17:0/17:0 standard solution, and caffeine-(trimethyl-13C) was used as the internal standard and directly infused into the mass spectrometer on positive ion mode. Intra-day and inter-day precision, accuracy, absolute recovery, and matrix effect were evaluated in three concentration levels in replicate (n=5). Limits of detection and quantification were estimated in the order of ng mL-1 with good linearity (r² >0.99), precision and accuracy (<15%), and absolute recovery values ranging from 90 to 110%. The mass spectra were submitted to the Global Natural Product Social Network (GNPS) platform for peak annotation. The partial least-square discriminant analysis (PLS-DA) was performed in all samples clustering the healthy samples and separating them from the cancer ones. The PLS-DA revealed that 15 lipids were responsible for describing the healthy group in the positive ion mode, while 8 lipids described the cancer one. In the negative ion mode, 10 lipids described the healthy group, while 10 lipids described the cancer one. Furthermore, cryosections of gingiva tissue (healthy and cancer ones) with 10 µm thickness were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate which lipids pointed by the PLS-DA delimit the cancer region. The MALDI-MSI analyses showed that the lipids responsible for OSCC group classification are more abundant in the cancer tissue compared to the healthy one. The extraction methodology reported here, and the MALDI-MSI as confirmation technique are adequate for classification of OSCC samples regarding lipid content. |
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Chaves, Andréa Rodrigueshttp://lattes.cnpq.br/6064014965252121Chaves, Andréa RodriguesJanfelt, ChristianPorcari, Andréia de MeloLima, Eliana MartinsColtro, Wendell Karlos Tomazellihttps://lattes.cnpq.br/4470407084894434Bernardo, Ricardo Alves2022-11-25T12:04:42Z2022-11-25T12:04:42Z2022-09-23Bernardo, Ricardo Alves. Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry. 2022. 225 f. Tese ( Doutorado em Química) - Universidade Federal de Goiás, Goiânia, 2022.http://repositorio.bc.ufg.br/tede/handle/tede/12452Oral squamous cell carcinoma (OSCC) is the most common oral cavity cancer, responsible for 90% of all cancers in the head-neck region, except for non-melanoma skin cancer. A fast, accurate, and cheap diagnosis is required to detect the presence of OSCC in the preliminary stages providing better chances of success in cancer treatment. In general, the diagnosis is performed using proteomics and histological data. However, lipids play a key role in cellular metabolism. Therefore, understanding the lipid differentiation between healthy and cancer tissues could be important to predict biomarkers candidates and improve the diagnosis system. Furthermore, a new methodology for simultaneous RNA and lipid extraction using TRIzol® solution was developed, and the lipid profile in both sample groups was studied. The samples stored in TRIzol® solution were homogenized and submitted to liquid-liquid microextraction (LLME) using chloroform as an extractive solvent. Therefore, the organic phase was collected and submitted to the Bligh &Dyer extraction. The simultaneous RNA and lipid extraction was validated according to the parameters described by the Brazilian Nation Health Surveillance Agency. An analytical curve of tissue in methanol and another one of tissue in TRIzol® solution were performed for method evaluation. The sample solutions were spiked with different concentrations of PC 17:0/17:0 standard solution, and caffeine-(trimethyl-13C) was used as the internal standard and directly infused into the mass spectrometer on positive ion mode. Intra-day and inter-day precision, accuracy, absolute recovery, and matrix effect were evaluated in three concentration levels in replicate (n=5). Limits of detection and quantification were estimated in the order of ng mL-1 with good linearity (r² >0.99), precision and accuracy (<15%), and absolute recovery values ranging from 90 to 110%. The mass spectra were submitted to the Global Natural Product Social Network (GNPS) platform for peak annotation. The partial least-square discriminant analysis (PLS-DA) was performed in all samples clustering the healthy samples and separating them from the cancer ones. The PLS-DA revealed that 15 lipids were responsible for describing the healthy group in the positive ion mode, while 8 lipids described the cancer one. In the negative ion mode, 10 lipids described the healthy group, while 10 lipids described the cancer one. Furthermore, cryosections of gingiva tissue (healthy and cancer ones) with 10 µm thickness were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate which lipids pointed by the PLS-DA delimit the cancer region. The MALDI-MSI analyses showed that the lipids responsible for OSCC group classification are more abundant in the cancer tissue compared to the healthy one. The extraction methodology reported here, and the MALDI-MSI as confirmation technique are adequate for classification of OSCC samples regarding lipid content.O carcinoma oral de células escamosas (OSCC) é o câncer da cavidade oral mais comum, responsável por cerca de 90% dos casos de câncer na região da cabeça-pescoço, com exceção do câncer de pele não-melanoma. Um diagnóstico rápido, preciso e barato é necessário para detectar a presença do OSCC em estágios recentes, aumentando assim, a chance de sucesso no tratamento. Em geral, os diagnósticos são feitos com base em análises proteômicas e histológicas, porém os lipídios desempenham importantes papeis no metabolismo celular. Portanto, entender a diferença lipídica entre tecidos saudáveis e com câncer é importante na predição de candidatos a biomarcadores na tentativa de antecipar o diagnóstico. Com isso, uma nova metodologia para extração simultânea de RNA e lipídios usando solução TRIzol® foi desenvolvida, e o perfil lipídico nos dois grupos de amostras foi estudado. As amostras armazenadas em TRIzol® foram homogeneizadas e submetidas a microextração líquido-líquido (LLME), utilizando clorofórmio como solvente extrator. Em seguida, a fase orgânica foi coletada e submetida à extração Bligh &Dyer. A extração simultânea de RNA e lipídios teve sua performance analítica avaliada de acordo com os parâmetros descritos pela Agência Nacional de Vigilância Sanitária. Para tanto, duas curvas analíticas foram construídas, sendo a primeira preparada utilizando tecido em metanol, e a segunda, tecido em solução TRIzol®. As amostras foram então dopadas com concentrações variáveis de solução padrão de PC 17:0/17:0, e cafeína-(trimetil-13C) utilizada como padrão interno, e introduzidas no sistema de espectrometria de massas por infusão direta no modo positivo de ionização. Ensaios de precisão e exatidão intra e inter-dias, recuperação absoluta e efeito matriz foram avaliados em três níveis de concentração em replicata (n=5). Os limites de detecção e quantificação foram estimados na ordem de ng mL-1 com linearidade (r²>0,99), precisão e exatidão (<15%), enquanto os valores de recuperação absoluta variaram de 90 a 110%. Os espectros de massas foram então submetidos à plataforma Global Natural Product Social Molecular Networking (GNPS) para anotação de picos. Análise discriminante por quadrados mínimos parciais (PLS-DA) foi realizada para a classificação dos grupos de amostras controle e com câncer. A PLS-DA revelou 15 lipídios responsáveis por descrever o grupo controle e 8 lipídios responsáveis por descrever o grupo com câncer, no modo positivo. Já para o modo negativo, 10 lipídios descreveram o grupo controle e 10 descreveram o grupo com câncer. Para validar a correlação entre os lipídios e as células tumorais, fatias de tecido gengival (controle e com câncer) de 10 µm de espessura foram analisadas por dessorção/ionização a laser assistida por matriz seguido de imageamento por espectrometria de massas (MALDI-MSI). As análises de MALDI-MSI demonstraram que os lipídios avaliados no extrato e responsáveis pela classificação do grupo OSCC estão mais abundantes ao longo do tecido tumoral comparado com o tecido saudável. O método de extração descrito neste estudo, bem como as confirmações realizadas por MALDI-MSI são adequadas para classificação de amostras de OSCC com relação ao seu conteúdo lipídico.Submitted by Dayane Basílio (dayanebasilio@ufg.br) on 2022-11-23T11:43:16Z No. of bitstreams: 2 Tese - Ricardo Alves Bernardo - 2022.pdf: 10467620 bytes, checksum: 67f460c8bccf8012ac7c65bc12d19f09 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Approved for entry into archive by Cláudia Bueno (claudiamoura18@gmail.com) on 2022-11-25T12:04:42Z (GMT) No. of bitstreams: 2 Tese - Ricardo Alves Bernardo - 2022.pdf: 10467620 bytes, checksum: 67f460c8bccf8012ac7c65bc12d19f09 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5)Made available in DSpace on 2022-11-25T12:04:42Z (GMT). No. of bitstreams: 2 Tese - Ricardo Alves Bernardo - 2022.pdf: 10467620 bytes, checksum: 67f460c8bccf8012ac7c65bc12d19f09 (MD5) license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Previous issue date: 2022-09-23Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESengUniversidade Federal de GoiásPrograma de Pós-graduação em Química (IQ)UFGBrasilInstituto de Química - IQ (RG)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessOral squamous cell carcinomaLipidsLLMEMALDIMass spectrometry imagingCarcinoma oral de células escamosasLipídiosImageamento químico por espectrometria de massasCIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA::ANALISE DE TRACOS E QUIMICA AMBIENTALOral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometryDiferenças de lipídios do carcinoma oral de células escamosas em amostras de gengiva empregando espectrometria de massasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis845005005005002910681reponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/3a0d5eeb-3240-4c35-b8b3-44294b6ca1a6/download8a4605be74aa9ea9d79846c1fba20a33MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.bc.ufg.br/tede/bitstreams/f4b75b79-1c7c-45be-8378-ede1c6b1fc11/download4460e5956bc1d1639be9ae6146a50347MD52ORIGINALTese - Ricardo Alves Bernardo - 2022.pdfTese - Ricardo Alves Bernardo - 2022.pdfapplication/pdf10467620http://repositorio.bc.ufg.br/tede/bitstreams/98227eaf-477c-44e4-8968-0173cf69c881/download67f460c8bccf8012ac7c65bc12d19f09MD53tede/124522022-11-25 09:04:42.432http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accessoai:repositorio.bc.ufg.br:tede/12452http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2022-11-25T12:04:42Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.pt_BR.fl_str_mv |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
dc.title.alternative.por.fl_str_mv |
Diferenças de lipídios do carcinoma oral de células escamosas em amostras de gengiva empregando espectrometria de massas |
title |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
spellingShingle |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry Bernardo, Ricardo Alves Oral squamous cell carcinoma Lipids LLME MALDI Mass spectrometry imaging Carcinoma oral de células escamosas Lipídios Imageamento químico por espectrometria de massas CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA::ANALISE DE TRACOS E QUIMICA AMBIENTAL |
title_short |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
title_full |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
title_fullStr |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
title_full_unstemmed |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
title_sort |
Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry |
author |
Bernardo, Ricardo Alves |
author_facet |
Bernardo, Ricardo Alves |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Chaves, Andréa Rodrigues |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6064014965252121 |
dc.contributor.referee1.fl_str_mv |
Chaves, Andréa Rodrigues |
dc.contributor.referee2.fl_str_mv |
Janfelt, Christian |
dc.contributor.referee3.fl_str_mv |
Porcari, Andréia de Melo |
dc.contributor.referee4.fl_str_mv |
Lima, Eliana Martins |
dc.contributor.referee5.fl_str_mv |
Coltro, Wendell Karlos Tomazelli |
dc.contributor.authorLattes.fl_str_mv |
https://lattes.cnpq.br/4470407084894434 |
dc.contributor.author.fl_str_mv |
Bernardo, Ricardo Alves |
contributor_str_mv |
Chaves, Andréa Rodrigues Chaves, Andréa Rodrigues Janfelt, Christian Porcari, Andréia de Melo Lima, Eliana Martins Coltro, Wendell Karlos Tomazelli |
dc.subject.eng.fl_str_mv |
Oral squamous cell carcinoma Lipids LLME MALDI Mass spectrometry imaging |
topic |
Oral squamous cell carcinoma Lipids LLME MALDI Mass spectrometry imaging Carcinoma oral de células escamosas Lipídios Imageamento químico por espectrometria de massas CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA::ANALISE DE TRACOS E QUIMICA AMBIENTAL |
dc.subject.por.fl_str_mv |
Carcinoma oral de células escamosas Lipídios Imageamento químico por espectrometria de massas |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA::ANALISE DE TRACOS E QUIMICA AMBIENTAL |
description |
Oral squamous cell carcinoma (OSCC) is the most common oral cavity cancer, responsible for 90% of all cancers in the head-neck region, except for non-melanoma skin cancer. A fast, accurate, and cheap diagnosis is required to detect the presence of OSCC in the preliminary stages providing better chances of success in cancer treatment. In general, the diagnosis is performed using proteomics and histological data. However, lipids play a key role in cellular metabolism. Therefore, understanding the lipid differentiation between healthy and cancer tissues could be important to predict biomarkers candidates and improve the diagnosis system. Furthermore, a new methodology for simultaneous RNA and lipid extraction using TRIzol® solution was developed, and the lipid profile in both sample groups was studied. The samples stored in TRIzol® solution were homogenized and submitted to liquid-liquid microextraction (LLME) using chloroform as an extractive solvent. Therefore, the organic phase was collected and submitted to the Bligh &Dyer extraction. The simultaneous RNA and lipid extraction was validated according to the parameters described by the Brazilian Nation Health Surveillance Agency. An analytical curve of tissue in methanol and another one of tissue in TRIzol® solution were performed for method evaluation. The sample solutions were spiked with different concentrations of PC 17:0/17:0 standard solution, and caffeine-(trimethyl-13C) was used as the internal standard and directly infused into the mass spectrometer on positive ion mode. Intra-day and inter-day precision, accuracy, absolute recovery, and matrix effect were evaluated in three concentration levels in replicate (n=5). Limits of detection and quantification were estimated in the order of ng mL-1 with good linearity (r² >0.99), precision and accuracy (<15%), and absolute recovery values ranging from 90 to 110%. The mass spectra were submitted to the Global Natural Product Social Network (GNPS) platform for peak annotation. The partial least-square discriminant analysis (PLS-DA) was performed in all samples clustering the healthy samples and separating them from the cancer ones. The PLS-DA revealed that 15 lipids were responsible for describing the healthy group in the positive ion mode, while 8 lipids described the cancer one. In the negative ion mode, 10 lipids described the healthy group, while 10 lipids described the cancer one. Furthermore, cryosections of gingiva tissue (healthy and cancer ones) with 10 µm thickness were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to investigate which lipids pointed by the PLS-DA delimit the cancer region. The MALDI-MSI analyses showed that the lipids responsible for OSCC group classification are more abundant in the cancer tissue compared to the healthy one. The extraction methodology reported here, and the MALDI-MSI as confirmation technique are adequate for classification of OSCC samples regarding lipid content. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-11-25T12:04:42Z |
dc.date.available.fl_str_mv |
2022-11-25T12:04:42Z |
dc.date.issued.fl_str_mv |
2022-09-23 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
Bernardo, Ricardo Alves. Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry. 2022. 225 f. Tese ( Doutorado em Química) - Universidade Federal de Goiás, Goiânia, 2022. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/12452 |
identifier_str_mv |
Bernardo, Ricardo Alves. Oral squamous cell carcinoma lipid differentiation in gingiva tissue by mass spectrometry. 2022. 225 f. Tese ( Doutorado em Química) - Universidade Federal de Goiás, Goiânia, 2022. |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/12452 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.program.fl_str_mv |
84 |
dc.relation.confidence.fl_str_mv |
500 500 500 500 |
dc.relation.department.fl_str_mv |
29 |
dc.relation.cnpq.fl_str_mv |
1068 |
dc.relation.sponsorship.fl_str_mv |
1 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Química (IQ) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Química - IQ (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
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UFG |
institution |
UFG |
reponame_str |
Repositório Institucional da UFG |
collection |
Repositório Institucional da UFG |
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MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFG - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
tasesdissertacoes.bc@ufg.br |
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1798044388190846976 |