Proteogenomics for Follicular Cell-derived Thyroid Cancer: Development of a Classification and Prognosis Prediction Model

Completed

• Conditions: Thyroid Cancer

• Registration Number: NCT06969768
• Lead Sponsor: Seoul National University Hospital

Brief Summary

This study aims to refine the molecular classification of thyroid cancer (TC) using a multi-omics approach. By identifying a novel gene set and applying decision-tree modeling, the study seeks to improve diagnostic accuracy and predict tumor progression in BRAFV600E-like and RAS-like TC subtypes. Protein biomarkers were validated via immunohistochemistry (IHC), with findings confirmed across external datasets.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-02-21
• Primary Completion: 2021-02-21
• Study Completion: 2024-02-28
• Study First Submitted: 2025-04-30
• Status Verified: 2025-05
• Study First Submitted QC: 2025-05-12
• Last Update Submitted: 2025-05-12
• Study First Posted: 2025-05-14
• Last Update Posted: 2025-05-14

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 275

Inclusion Criteria

• Patients with a confirmed clinical diagnosis of thyroid cancer
• Availability of surgically resected thyroid tissue suitable for omics analysis

Exclusion Criteria

• Patients who have received chemotherapy for other malignancies

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Classification Accuracy of Gene-Based Model	1 year	The accuracy of the decision-tree model using specific gene set to classify thyroid cancer into BRAFV600E-like, RAS-like, and NT (normal thyroid) -like subtypes. Model performance will be evaluated using accuracy, sensitivity, specificity, and Cohen's kappa.

Trial Locations

Locations (1)

Seoul National University Hospital; 🇰🇷 Seoul, Korea, Republic of