Análise e classificação da expressão gênica durante o Eritema Nodoso Hansênico através de dados de microarranjo

• Ano de defesa: 2017
• Autor(a) principal: Almeida, Karla Lopes de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Biomédica; UFRJ
• Palavras-chave em Português: Engenharia biomédica; Micro-arranjo; Classificação gênica; Eritema Nodoso Hansênico; Hanseníase; CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA
• Link de acesso: http://hdl.handle.net/11422/10113

Leprosy is a chronic infectious disease that affects millions of people around the world. Currently, the identification of leprosy reactions is of importance in researching because it is a neglected disease. These reactions are the ones that cause the greatest sequels in people with leprosy. However, a diagnostic method that predicts the reaction has not yet been obtained, prior to the appearance of the signs and symptoms. The objective of this work is to propose a methodology to analyze and classify the gene expression present during the reaction stage Erythema Nodosum Leprosum (ENL) through microarray data. For the application of this methodology, data of the expression levels of microarray experiments with ENL and Lepromatous Leprosy (LL) patients were used. We used three statistical techniques (Student’s t test with fold-change, Differential Method - Principal Components Analisys (DMPCA) and linear model microarray analisys-limma), to identify 53 differentially expressed genes. These genes were considered the most significant for the classification between the ENH and LL groups and they were used to create a predictive mathematical model using logistic regression modeling. Our methodology identified genes that were previously described in the literature. The logistic regression model proposed using these genes proved to be efficient when used in a new dataset. The model presented an accuracy of 92% and a specificity of 83.3%.Também disponível on-line.