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Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors
Author(s) -
Mariana Bisarro dos Reis,
Mateus Camargo BarrosFilho,
Fábio Albuquerque Marchi,
Caroline Moraes Beltrami,
Hellen Kuasne,
Clóvis Antônio Lopes Pinto,
Srikant Ambatipudi,
Zdenko Herceg,
Luiz Paulo Kowalski,
Sílvia Regina Rogatto
Publication year - 2017
Publication title -
the journal of clinical endocrinology and metabolism
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.206
H-Index - 353
eISSN - 1945-7197
pISSN - 0021-972X
DOI - 10.1210/jc.2017-00881
Subject(s) - dna methylation , computational biology , profiling (computer programming) , thyroid tumors , dna profiling , classifier (uml) , genome , biology , thyroid , dna , cancer research , thyroid cancer , computer science , genetics , artificial intelligence , gene , gene expression , operating system
Context: Even though the majority of well-differentiated thyroid carcinoma (WDTC) is indolent, a number of cases display an aggressive behavior. Cumulative evidence suggests that the deregulation of DNA methylation has the potential to point out molecular markers associated with worse prognosis. Objective: To identify a prognostic epigenetic signature in thyroid cancer. Design: Genome-wide DNA methylation assays (450k platform, Illumina) were performed in a cohort of 50 nonneoplastic thyroid tissues (NTs), 17 benign thyroid lesions (BTLs), and 74 thyroid carcinomas (60 papillary, 8 follicular, 2 Hürthle cell, 1 poorly differentiated, and 3 anaplastic). A prognostic classifier for WDTC was developed via diagonal linear discriminant analysis. The results were compared with The Cancer Genome Atlas (TCGA) database. Results: A specific epigenetic profile was detected according to each histological subtype. BTLs and follicular carcinomas showed a greater number of methylated CpG in comparison with NTs, whereas hypomethylation was predominant in papillary and undifferentiated carcinomas. A prognostic classifier based on 21 DNA methylation probes was able to predict poor outcome in patients with WDTC (sensitivity 63%, specificity 92% for internal data; sensitivity 64%, specificity 88% for TCGA data). High-risk score based on the classifier was considered an independent factor of poor outcome (Cox regression, P < 0.001). Conclusions: The methylation profile of thyroid lesions exhibited a specific signature according to the histological subtype. A meaningful algorithm composed of 21 probes was capable of predicting the recurrence in WDTC.

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