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Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From
1Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China.
Frontiers in Genetics
|January 31, 2022
Summary
Researchers identified a consistent gene signature to accurately differentiate benign from malignant thyroid nodules. This molecular signature, derived from combined transcriptomic data, improves diagnostic reliability beyond traditional methods.
Area of Science:
- Genomics
- Oncology
- Molecular Biology
Background:
- Thyroid nodules are common, but distinguishing malignant from benign types is challenging with current fine-needle biopsy methods.
- Existing molecular signatures for thyroid nodules lack consistency and clinical applicability.
- Improved diagnostic accuracy is crucial for effective thyroid cancer management.
Purpose of the Study:
- To identify a consistent and reliable gene signature for discriminating benign from malignant thyroid nodules.
- To develop a high-performance classification model for thyroid nodule diagnosis.
- To provide new insights into the molecular pathogenesis of malignant thyroid nodules.
Main Methods:
- Integrated five independent transcriptomic studies for a robust dataset.
- Applied feature selection (Student's t-test, fold change) to identify differentially expressed genes (DEGs).
- Utilized weighted gene co-expression network analysis (WGCNA) to find hub genes and identified key genes through intersection analysis.
Main Results:
- Identified 279 DEGs and 454 hub genes, with four key genes (ST3GAL5, NRCAM, MT1F, PROS1) validated in an independent dataset.
- These four genes are implicated in the pathogenesis of malignant thyroid nodules.
- Developed a high-performance classification model for thyroid nodule discrimination.
Conclusions:
- The identified four-gene signature offers a consistent and potentially clinically applicable method for thyroid nodule diagnosis.
- This molecular approach enhances diagnostic accuracy compared to conventional methods.
- The findings contribute to a better understanding of thyroid cancer's molecular basis.
