Insights into immune responses in oral cancer through proteomic analysis of saliva and salivary extracellular vesicles

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOThe development and progression of oral cavity squamous cell carcinoma (OSCC) involves complex cellular mechanisms that contribute to the low five-year survival rate of approximately 20% among diagnosed patients. However, the biological processes essential to tumor progression are not completely understood. Therefore, detecting alterations in the salivary proteome may assist in elucidating the cellular mechanisms modulated in OSCC and improve the clinical prognosis of the disease. The proteome of whole saliva and salivary extracellular vesicles (EVs) from patients with OSCC and healthy individuals were analyzed by LC-MS/MS and label-free protein quantification. Proteome data analysis was performed using statistical, machine learning and feature selection methods with additional functional annotation. Biological processes related to immune responses, peptidase inhibitor activity, iron coordination and protease binding were overrepresented in the group of differentially expressed proteins. Proteins related to the inflammatory system, transport of metals and cellular growth and proliferation were identified in the proteome of salivary EVs. The proteomics data were robust and could classify OSCC with 90% accuracy. The saliva proteome analysis revealed that immune processes are related to the presence of OSCC and indicate that proteomics data can contribute to determining OSCC prognosis.The development and progression of oral cavity squamous cell carcinoma (OSCC) involves complex cellular mechanisms that contribute to the low five-year survival rate of approximately 20% among diagnosed patients. However, the biological processes essential to tumor progression are not completely understood. Therefore, detecting alterations in the salivary proteome may assist in elucidating the cellular mechanisms modulated in OSCC and improve the clinical prognosis of the disease. The proteome of whole saliva and salivary extracellular vesicles (EVs) from patients with OSCC and healthy individuals were analyzed by LC-MS/MS and label-free protein quantification. Proteome data analysis was performed using statistical, machine learning and feature selection methods with additional functional annotation. Biological processes related to immune responses, peptidase inhibitor activity, iron coordination and protease binding were overrepresented in the group of differentially expressed proteins. Proteins related to the inflammatory system, transport of metals and cellular growth and proliferation were identified in the proteome of salivary EVs. The proteomics data were robust and could classify OSCC with 90% accuracy. The saliva proteome analysis revealed that immune processes are related to the presence of OSCC and indicate that proteomics data can contribute to determining OSCC prognosis5FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO2009/54067-3; 2010/19278-0470549/2011-4; 301702/2011-0; 470268/2013-1

Discovery-based proteomics for the search for therapeutic targets and potential biomarkers using univariate and multivariate analyzes

Orientador: Adriana Franco Paes LemeDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de BiologiaResumo: Biomarcadores são essenciais no diagnóstico, prognóstico e desenvolvimento de fármacos. Sua pesquisa deve envolver alta sensibilidade e seletividade, definindo resultados claros de confirmação ou exclusão da doença, bem como alta qualidade na metodologia analítica, em métodos computacionais e estatísticos. O sucesso de biomarcadores em testes clínicos tem sido pequeno e uma das razões é que poucos candidatos à biomarcadores passam por uma validação rigorosa por meio de modelos estatísticos, como também pequenos números de amostras biológicas, variabilidade biológica resultando em falsos positivos. Dessa forma, o objetivo desse estudo é aplicar modelos estatísticos de análise que ajudem a selecionar biomarcadores candidatos na fase de descoberta, classificá-los e validá-los para posterior aplicação em uma nova fase de verificação, como proteômica baseada em alvos. Para isso, foram analisadas amostras de saliva de pacientes com e sem carcinoma de células escamosas (CEC) contendo 4 proteínas recombinantes em concentrações crescentes. Após a identificação e quantificação das proteínas utilizando o algoritmo de busca do Andromeda integrado no MaxQuant, os dados foram analisados pelos métodos de Kruskal-Wallis, Wilcoxon, Nearest Shrunken Centroid (NSC), Random Forest e Support Vector Machine ¿ Recursive Feature Elimination (SVM-RFE), seguidos de métodos de validação como validação cruzada, permutação e Curvas de Características de Operação do Receptor (ROC). Os resultados mostram que as análises de Kruskal-Wallis e Wilcoxon foram capazes de detectar as proteínas marcadoras adicionadas nas amostras de saliva em concentrações crescentes como verdadeiros positivos. Houve melhora na classificação das amostras para as análises de Random Forest e NSC quando proteínas as proteínas foram filtradas pelo valor de p, com exceção para a análise de SVM-RFE. Da mesma forma, em relação ao ranqueamento, as proteínas marcadoras tiveram uma melhora na posição do ranqueamento quando os dados foram filtrados pelo valor de p para NSC, mas não para SVM-RFE. Por meio da análise das curvas ROC foi possível verificar o limite de sinal e ruído para discriminar corretamente cada classe de paciente. Os resultados sugerem que trabalhar com os dados filtrados pelo valor de p aumenta o acerto na classificação dos pacientes para as análises de Random Forest e NSC, entretanto, o mesmo não acontece para a análise utilizando SVM-RFEAbstract: Biomarkers are essential in diagnosis, prognosis and drug development. It demands high sensibility and specificity to obtain results that confirm or exclude diseases, as well as high quality in analytical, computational and statistical methods. However, the success of biomarkers in clinical trials has been limited, and some reasons are that few candidate biomarkers are rigorously validated by statistical models, and likely, the use of small numbers of biological samples together with high biological variability results in many false positives. Therefore, the goal this study is to apply statistical models to help select candidate biomarkers in discovery phase and classify them for further application in a new verification step such as targeted proteomics. For that, human saliva samples originated from patients with and without Oral Squamous cell Carcinoma (OSCC) were analyzed by LC-MSMS with four recombinant proteins spiked in the samples. After protein identification and quantification performed with Andromeda search algorithm within MaxQuant, the data were analyzed by Kruskal-Wallis, Wilcoxon, Nearest Shrunken Centroid (NSC), Random Forest and Support Vector Machine ¿ Recursive Feature Elimination (SVM-RFE), followed by validation analysis such as cross-validation, permutation and ROC curves. The results showed that Kruskal-Wallis and Wilcoxon were able to detect the spiked recombinant proteins in saliva samples in increasing concentration as true positives. There was an improvement in sample classification in Random Forest and NSC analyses when the input data were proteins that were previously filtered by p-value, except for SVM-RFE. Similarly, in relation to the ranking analysis, the recombinant proteins improved their position when previously filtered by the p-value for the NSC analysis, but not for the SVM-RFE. ROC curves showed the signal noise limit for the correct discrimination of each patient class. The results suggest that data filtered by p-value improve the correct classification of patients for Random Forest and NSC analysis, but not for SVM-RFE analysisMestradoFármacos, Medicamentos e Insumos para SaúdeMestra em Ciências001CAPE

Deregulation of desmosomal proteins and extracellular matrix proteases in odontogenic keratocyst

OBJECTIVE : Odontogenic keratocyst (OKC) is a benign lesion that tends to recur after surgical treatment. In an attempt to clarify the molecular basis underlining the OKC pathobiology, we aimed to analyze its proteomic profile. MATERIALS AND METHODS : We compared the proteomic profiles of five OKC and matched normal oral mucosa by using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Then, we performed enrichment analysis and a literature search for the immunoexpression of the proteomics targets. RESULTS : We identified 1,150 proteins and 72 differently expressed proteins (log2 fold change ≥ 1.5; p < .05). Twenty-seven peptides were exclusively detected in the OKC samples. We found 35 enriched pathways related to cell differentiation and tissue architecture, including keratinocyte differentiation, keratinization, desmosome, and extracellular matrix (ECM) organization and degradation. The immunoexpression information of 11 out of 50 proteins identified in the enriched pathways was obtained. We found the downregulation of four desmosomal proteins (JUP, PKP1, PKP3, and PPL) and upregulation of ECM proteases (MMP-2, MMP-9, and cathepsins). CONCLUSIONS : Proteomic analysis strengthened the notion that OKC cells have a similar proteomic profile to oral keratinocytes. Contextual investigation of the differentially expressed proteins revealed the deregulation of desmosome proteins and ECM degradation as important alterations in OKC pathobiology.Conselho Nacional de Desenvolvimento Científico e Tecnológico and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior.http://www.wileyonlinelibrary.com/journal/odihj2022Oral Pathology and Oral Biolog