Uso da técnica LC-HRMS associada a métodos quimiométricos (PLS-DA e SVM) para detectar câncer de próstata através da urina

• Ano de defesa: 2023
• Autor(a) principal: Silva, Thifanny Tiburcio Pereira da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Química
• Palavras-chave em Português: Câncer de próstata; Prostate cancer; LC-HRMS; Quimiometria; Chemometrics; Biomarcadores; Biomarkers; CNPQ::CIENCIAS EXATAS E DA TERRA::QUIMICA::QUIMICA ANALITICA; Química; Próstata - Câncer
• Link de acesso: https://repositorio.ufu.br/handle/123456789/39002; http://doi.org/10.14393/ufu.di.2023.478

Prostate cancer (PC) is the most frequent type of cancer among the male population in Brazil, comprising 30% of the diagnoses of the disease in the country. Prior diagnosis is a way to reduce the number of deaths and thus obtain greater chances of cure. The most applied clinical tests are Prostate Specific Antigen (PSA) and digital rectal examination. Biopsy is indicated when it is necessary to visualize the identified lesion more closely. Although it is a common disease among the male population, it is a serious health problem, because when it is discovered in the initial phase, the chance of cure is greater than 90%, therefore, it is necessary to develop new efficient clinical tests for the diagnosis of the disease. , using a non-invasive, rapid and reproducible approach to prostate cancer. The liquid chromatography technique coupled with high resolution mass spectrometry (LC-HRMS) has become an important analytical tool in metabolomics, as it is a technique that presents high sensitivity and selectivity, however, it presents high complexity of the matrix, therefore , it is essential to develop analytical methods to overcome the challenges proposed in this project in combination with chemometric methods, aiming to correlate the information extracted from spectra with the internal properties of diagnosing patients with prostate cancer through urine analysis. The use of chemometric methods of discriminant analysis by partial least squares (PLS-DA) and support vector machine (SVM) to extract the information contained in the mass spectra allowed the development of stable, robust and easy to interpret models, capable of discriminate samples from patients with prostate cancer and healthy patients with 100% efficiency. For the PLS-DA model, the results obtained values of RMSEC = 0.54; RMSECV = 0.64; RMSEP = 0.57; Sensitivity = 1 and Specificity = 1. As it provides the most significant variables through the graph of weights, the PLS-DA chemometric method becomes the best to be applied in the search for new biomarkers and alternative methods for early detection of the disease.