Identificação de macromoléculas em linhagens celulares da classificação molecular do câncer de mama via espectroscopia vibracional e técnicas de recursividade

• Ano de defesa: 2019
• Autor(a) principal: Powrosnek, Letícia Costa Brambilla
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Mamas - Câncer - Identificação; Mamas - Câncer - Diagnóstico; Análise espectral; Mamas - Câncer - Prevenção; Moléculas - Classificação; Ciências médicas; Células - Metabolismo; Engenharia elétrica; Breast - Cancer - Identification; Breast - Cancer - Diagnosis; Spectrum analysis; Breast - Cancer - Prevention; Molecules - Classification; Medical sciences; Cell metabolism; Electric engineering; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::TELECOMUNICACOES::TEORIA ELETROMAGNETICA, MICROONDAS, PROPAGACAO DE ONDAS, ANTENAS; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/4483

Cancer is a collection of over 100 different types of diseases that have in common the disordered and unbridled growth of abnormal cells with invasive potential. Breast cancer is the most common cancer in women, excluding non-melanoma skin tumors, is the fifth leading cause of death compared to all other cancers. The gold standard for its diagnosis is through imaging diagnostic technologies, which allow the localization and sizing of the tumor, and by immunohistochemical and morphological exams, however tumors that have the same classical pathological characteristics may have distinct behaviors in their biology. Molecular classification correlates gene expression profiles with relevant clinical parameters, where it classifies tumors into 5 intrinsic subtypes: luminal A, luminal B, HER-2, basal and normal- like, but it is a complex and costly technique, which makes its use in clinical practice difficult, encouraging the development of new diagnostic methods. This paper presents the study of the identification of macromolecules in strains derived from patients of different breast cancer subtypes. Data were acquired on a Varian 640-IR FTIR spectrophotometer using the ATR configuration in 3 cell lines: MCF7 (classified as luminal A), MDA-MB-231 (classified as basal) and BT-474 (classified as luminal B). The experiment was carried out with the cells cultivated in vitro without previous preparation, which allowed the study of the biochemical behavior of living cells. From the spectral data, recurrence graphs were generated for each sample and quantitative recurrence analyzes were performed to identify the lipid, protein and nucleic acid macromolecules of the samples. However it was not possible to characterize each lineage because there is no known pattern. The results showed the feasibility of using RQA to analyze FTIR spectra of cell samples in vitro. In addition, the statistical analysis of the quantities resulting from the RQA made it possible to distinguish macromolecules according to all statistical protocols. Future work proposes the refinement of spectral acquisition and analysis techniques in order to determine an optical profile that characterizes molecular classification cell lines, helping to accurately and early diagnose breast cancer.