Prognostic Classifier Based on Genome-Wide DNA Methylation Profiling in Well-Differentiated Thyroid Tumors

Made available in DSpace on 2018-12-11T17:23:43Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-11-01Context: 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.International Research Center, CIPE, A.C. Camargo Cancer Center and National Institute of Science and Technology in Oncogenomics, São Paulo 01509-010, SP, BrazilDepartment of Urology, Faculty of Medicine, UNESP, São Paulo State University, Botucatu 18618-970, SP, BrazilDepartment of Pathology, A.C. Camargo Cancer Center, São Paulo 01509-010, SP, BrazilEpigenetics Group; International Agency for Research on Cancer (IARC), Lyon 69372, FranceMRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, United KingdomDepartment of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo 01509-010, SP, BrazilDepartment of Clinical Genetics, Vejle Hospital and Institute of Regional Health Research, University of Southern Denmark, Vejle, 7100, Denmar

Loss of DNA methylation is related to increased expression of miR-21 and miR-146b in papillary thyroid carcinoma

Abstract Background DNA methylation in miRNA genes has been reported as a mechanism that may cause dysregulation of mature miRNAs and consequently impact the gene expression. This mechanism is largely unstudied in papillary thyroid carcinomas (PTC). Methods To identify differentially methylated miRNA-encoding genes, we performed global methylation analysis (Illumina 450 K), integrative analysis (TCGA database), data confirmation (pyrosequencing and RT-qPCR), and functional assays. Results Methylation analysis revealed 27 differentially methylated miRNA genes. The integrative analyses pointed out miR-21 and miR-146b as potentially regulated by methylation (hypomethylation and increased expression). DNA methylation and expression patterns of miR-21 and miR-146b were confirmed as altered, as well as seven of 452 mRNAs targets were down-expressed. The combined methylation and expression levels of miR-21 and miR-146b showed potential to discriminate malignant from benign lesions (91–96% sensitivity and 96–97% specificity). An increased expression of miR-146b due to methylation loss was detected in the TPC1 cell line. The miRNA mimic transfection highlighted putative target mRNAs. Conclusions The increased expression of miR-21 and miR-146b due to loss of DNA methylation in PTC resulted in the disruption of the transcription machinery and biological pathways. These miRNAs are potential diagnostic biomarkers, and these findings provide support for future development of targeted therapies