A three-gene panel that distinguishes benign from malignant thyroid nodules

Area of Science:

• Molecular diagnostics
• Oncology
• Genomics

Background:

• Preoperative diagnosis of thyroid tumors is challenging due to inconclusive fine-needle aspiration biopsies.
• High-throughput molecular diagnostics show promise but have limited clinical application.

Purpose of the Study:

• To identify a small gene subset for a practical and inexpensive thyroid cancer diagnostic tool.
• To develop a robust gene signature for distinguishing benign from malignant thyroid tumors.

Main Methods:

• A two-step feature selection method using Linear Models for Microarray Data (LIMMA) and Bayesian model averaging.
• Discovery of a three-gene signature (DPP4, SCG5, CA12) in public datasets.
• Validation using independent datasets, quantitative polymerase chain reaction (QPCR), and immunohistochemistry.

Main Results:

• The three-gene signature achieved accuracies of 85.7%, 78.8%, and 85.7% in independent public datasets.
• Experimental validation using QPCR on 70 thyroid samples yielded 94.3% accuracy.
• Immunohistochemistry confirmed differential protein expression for the three genes in thyroid samples.

Conclusions:

• A robust three-gene signature (DPP4, SCG5, CA12) effectively differentiates benign from malignant thyroid tumors.
• This molecular signature provides a promising tool for daily clinical use in thyroid cancer diagnosis.