Comprehensive Proteomics and Machine Learning Analysis to Distinguish Follicular Adenoma and Follicular Thyroid Carcinoma from Indeterminate Thyroid Nodules.

ABSTRACT:

Backgruound

The preoperative diagnosis of follicular thyroid carcinoma (FTC) is challenging because it cannot be readily distinguished from follicular adenoma (FA) or benign follicular nodular disease (FND) using the sonographic and cytological features typically employed in clinical practice.

Methods

We employed comprehensive proteomics and machine learning (ML) models to identify novel diagnostic biomarkers capable of classifying three subtypes: FTC, FA, and FND. Bottom-up proteomics techniques were applied to quantify proteins in formalin-fixed, paraffin-embedded (FFPE) thyroid tissues. In total, 202 FFPE tissue samples, comprising 62 FNDs, 72 FAs, and 68 FTCs, were analyzed.

Results

Close spectrum-spectrum matching quantified 6,332 proteins, with approximately 9%

SUBMITTER: Ahn HS 

PROVIDER: S-EPMC12409165 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature