Perfil de metilação do DNA em lesões tireoidianas

• Autor(a) principal: Reis, Mariana Bisarro dos [UNESP]
• Data de Publicação: 2015
• Tipo de documento: Tese
• Idioma: por
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://hdl.handle.net/11449/144080
• topic: Tireoide - Cãncer; Sistema endócrino; Metilação de DNA; Neoplasias; Thyroid gland - Diseases

Thyroid cancer (TC) is the most prevalent type of endocrine cancer. Papillary thyroid carcinoma (PTC) comprises 80-85% of the diagnosed thyroid cancers, followed by follicular (FTC), poorly differentiated (PDTC) and anaplastic carcinomas (ATC). Diagnosis of thyroid carcinomas, especially of well-differentiated carcinomas is a challenge due to morphological similarities between these tumors and benign lesions. The aim of this study was to evaluate the methylation profile to identify diagnostic markers involved in benign lesions and in different histological subtypes of carcinomas. Moreover, a search for reliable molecular prognostic markers was also performed in TC. The study included 17 benign lesions (8 adenomas, 6 goiters and 3 thyroiditis), 60 PTCs, 8 FTCs, 2 Hürthle cell carcinomas (HCC), 1 PDTC and 3 ATC, as well as 50 non-neoplastic tissues (NT) obtained from patients who had PTC. Differential methylation analyzes were performed using the Infinium® Human Methylation450 BeadChip microarray (Illumina). In the first stage of the study, the results of differentially methylated probes were used in the development of diagnostic classifier algorithm. In second step, the methylation profile of benign lesions and tumor subtypes was compared to data from non-neoplastic tissues. Only probes significantly altered in the current study and those confirmed by GEO data (Gene Expression Omnibus) were selected for the development of the algorithms. Three diagnostic algorithms were developed based on differential methylation of nine probes selected from area under the curve of 0.75 for BTL classifier and 0.90 for FTC and PTC classifiers and multivariate analysis. It was also applied linear classification methods. Application of the algorithm diagnosis allowed the correct classification of non-neoplastic tissues, benign and malignant lesions (sensitivity: 91.9% and specificity: 76.5%). The same strategy was performed using the GEO database...