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

Integrative Multi-Omics Refines the Molecular Subtypes of Thyroid Cancers and Enhances Cancer-Progression Prediction

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06969768

Enrollment

275

Registered

2025-05-14

Start date

2018-02-21

Completion date

2024-02-28

Last updated

2025-05-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Thyroid Cancer

Keywords

Thyroid cancer, Proteogenomics

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
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.

Countries

South Korea

Outcome results