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Synthesis and Regulation of Thyroid Hormones

Synthesis and Regulation of Thyroid Hormones

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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Updated: Sep 13, 2025

In Vivo Inhibition of MicroRNA to Decrease Tumor Growth in Mice

In Vivo Inhibition of MicroRNA to Decrease Tumor Growth in Mice

Published on: August 23, 2019

Development and validation of mRNA expression-based classifiers to predict low-risk thyroid tumors.

Allan Golding¹, David Bimston², Emma Namiranian³

¹Memorial Healthcare System, Interventional Endocrinology, Hollywood, FL, United States.

Frontiers in Endocrinology

|July 31, 2025

Summary

This summary is machine-generated.

New mRNA classifiers can accurately rule out thyroid cancer invasion and lymph node metastasis preoperatively. This advancement aids in personalizing surgery, reducing complications and overtreatment for thyroid cancer patients.

Keywords:

Afirma machine learning molecular diagnostics thyroid cancer thyroid nodule thyroid tumor prognosis

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Area of Science:

Oncology
Genomics
Molecular Diagnostics

Background:

Thyroid nodule molecular variants offer population-level prognostic data but show variable clinical behavior.
Exome-enriched gene expression analysis may surpass limitations of point mutation or fusion-based models.
Accurate preoperative prediction of thyroid cancer invasion and metastasis is crucial for treatment planning.

Purpose of the Study:

To develop and validate mRNA-based classifiers for preoperative risk assessment of thyroid tumor invasion.
To create classifiers capable of ruling out lymph node metastasis in thyroid cancer patients.
To improve personalized surgical approaches and reduce overtreatment in thyroid cancer management.

Main Methods:

Retrospective cohort study using histopathology reports from thyroid cancer patients.
Development of mRNA expression-based classifiers using literature-derived signatures and differentially expressed genes.
Machine learning algorithms employed for classifier development and validation on independent cohorts.

Main Results:

Classifiers demonstrated high negative predictive values (NPVs) for ruling out invasion (97.6% development, 99% validation) and lymph node metastasis (98.6% development, 100% validation).
Significant percentages of patients were classified as low-risk for invasion and metastasis, enabling potential surgical de-escalation.
Validation cohort confirmed the high accuracy of the mRNA classifiers in a real-world clinical setting.

Conclusions:

Gene expression-based classifiers can confidently rule out thyroid tumor invasion and lymph node metastasis preoperatively.
These classifiers support personalized surgical strategies, potentially decreasing overtreatment and associated complications.
Reduced surgical intervention and improved patient outcomes, including fewer instances of postoperative hypothyroidism, are anticipated.