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

Seoul National University Hospital275 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

275

Conditions

Thyroid Cancer

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Locations (1)

Seoul National University Hospital

Seoul, South Korea

Outcomes

Primary Outcomes

Classification Accuracy of Gene-Based Model

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.

Time frame: 1 year

Data from ClinicalTrials.gov

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