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Related Concept Videos

Epigenetic Regulation01:37

Epigenetic Regulation

3.1K
Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
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Synthesis and Regulation of Thyroid Hormones01:20

Synthesis and Regulation of Thyroid Hormones

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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
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Related Experiment Video

Updated: Jul 24, 2025

Continuous Fluorescence-Based Endonuclease-Coupled DNA Methylation Assay to Screen for DNA Methyltransferase Inhibitors
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Classification of Thyroid Tumors Based on DNA Methylation Patterns.

Vicente Rodrigues Marczyk1,2, Mariana Recamonde-Mendoza3,4, Ana Luiza Maia1,2

  • 1Thyroid Unit, Endocrine Division, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil.

Thyroid : Official Journal of the American Thyroid Association
|July 1, 2023
PubMed
Summary

DNA methylation patterns classify thyroid tumors into three subtypes: normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like. This epigenetic classification correlates with histological diagnosis and genomic drivers, aiding in thyroid neoplasm understanding.

Keywords:
DNA methylationpapillary thyroid carcinomathyroid adenomathyroid cancerthyroid carcinomathyroid neoplasmthyroid noduleunsupervised machine learning

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Area of Science:

  • Epigenetics
  • Oncology
  • Bioinformatics

Background:

  • DNA methylation alterations are stable epigenetic events and potential clinical biomarkers.
  • Understanding methylation patterns in follicular cell-derived thyroid neoplasms is crucial for classification and disease subtyping.

Purpose of the Study:

  • To analyze DNA methylation patterns in thyroid neoplasms using unsupervised machine learning.
  • To identify distinct methylation-based subtypes of thyroid tumors.
  • To correlate methylation subtypes with histological diagnosis and genomic drivers.

Main Methods:

  • Utilized an unsupervised machine learning algorithm for class discovery based solely on DNA methylation data.
  • Analyzed 810 thyroid samples, including benign, malignant, and normal tissues, for discovery and validation.
  • Classified samples into subtypes without prior clinical or pathological information.

Main Results:

  • Identified three distinct methylation subtypes: normal-like, follicular-like, and papillary thyroid carcinoma (PTC)-like.
  • Follicular-like subtype included follicular adenomas/carcinomas and oncocytic tumors; PTC-like subtype included classic and tall cell PTC.
  • Methylation subtypes strongly correlated with histological diagnosis and specific genomic drivers (BRAF in PTC-like, RAS in follicular-like).
  • Follicular variant PTC (FVPTC) showed heterogeneity, splitting into both follicular-like (enriched for RAS) and PTC-like (enriched for BRAF/RET) methylation patterns.

Conclusions:

  • DNA methylation patterns provide a robust basis for classifying thyroid neoplasms.
  • The unsupervised approach offers novel insights into epigenetic alterations and tumor heterogeneity.
  • Methylation-based subtyping, particularly for FVPTC, may reveal distinct disease entities and guide clinical management.