Made available in DSpace on 2016-09-27T13:40:04Z (GMT). No. of bitstreams: 0
Previous issue date: 2015-05-27. Added 1 bitstream(s) on 2016-09-27T13:45:15Z : No. of bitstreams: 1
000869033_20170527.pdf: 824646 bytes, checksum: 00f5f8bbdd7abcbefcbd2d042d519568 (MD5) Bitstreams deleted on 2017-06-02T11:56:26Z: 000869033_20170527.pdf,. Added 1 bitstream(s) on 2017-06-02T11:57:14Z : No. of bitstreams: 1
000869033.pdf: 3301498 bytes, checksum: de14b09badf78ded8a950b747525f42b (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / O câncer de tireoide (CT) é a neoplasia mais comum do sistema endócrino. O carcinoma papilífero da tireoide (CPT) compreende 80-85% dos casos, seguido dos carcinomas foliculares (CFT), pouco diferenciados (CPDT) e anáplasicos (CAT). O diagnóstico dos CT, principalmente nos casos bem diferenciados, ainda é um desafio devido a semelhanças morfológicas compartilhadas por esses tumores e lesões benignas (LBT). O objetivo desse estudo foi avaliar o perfil de metilação do DNA para identificar marcadores epigenéticos envolvidos no desenvolvimento das lesões benignas e dos diferentes subtipos histológicos de carcinomas. Além disso, buscou-se identificar marcadores prognósticos nos CT. Foram incluídos nesse estudo 17 lesões benignas da tireoide (8 adenomas, 6 bócios tireoideanos e 3 tireoidites), 60 CPT, 8 CFT, 2 carcinomas de células de Hurthle (CCH), 1 CPDT e 3 CAT, além de 50 tecidos não neoplásicos (TN) obtidos dos pacientes que tiveram CPT. As análises de metilação diferencial foram realizadas utilizando a plataforma microarray Infinium® Human Methylation450 BeadChip (Illumina). Na primeira etapa do estudo, os resultados obtidos de sondas diferencialmente metiladas foram utilizados na construção de um algoritmo útil como classificador diagnóstico. Na segunda etapa, o perfil de metilação do DNA das lesões benignas e dos diferentes subtipos tumorais foi comparado aos dados de tecidos não neoplásicos. Somente sondas significativamente alteradas no presente estudo e aquelas confirmadas no GEO (Gene Expression Omnibus) foram selecionadas para a construção de algoritmos. Foram delineados três algoritmos diagnósticos baseados na metilação diferencial de nove sondas selecionadas a partir de área abaixo da curva de 0,75 para o classificador de LBT e 0,90 para os classificadores CFT e CPT além de análise multivariada. Foram também aplicados métodos lineares de classificação. A aplicação do algoritmo... / 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...
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.unesp.br:11449/144080 |
| Date | 27 May 2015 |
| Creators | Reis, Mariana Bisarro dos [UNESP] |
| Contributors | Universidade Estadual Paulista (UNESP), Rogatto, Silvia Regina [UNESP] |
| Publisher | Universidade Estadual Paulista (UNESP) |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Format | 1 CD-ROM |
| Source | Aleph, reponame:Repositório Institucional da UNESP, instname:Universidade Estadual Paulista, instacron:UNESP |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | -1, -1 |
Page generated in 0.0025 seconds