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PLoS One. 2016 Oct 24;11(10):e0164570. doi: 10.1371/journal.pone.0164570. eCollection 2016.

Classification of Benign and Malignant Thyroid Nodules Using a Combined Clinical Information and Gene Expression Signatures.

Zheng B1,2, Liu J3,4,5, Gu J1,6, Du J7, Wang L7, Gu S8, Cheng J8, Yang J4,5, Lu H1,6,9.

Author information

1
Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
2
Department of Laboratory Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
3
Department of Otolaryngology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
4
Department of Otolaryngology-Head and Neck Surgery, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, China.
5
Ear Institute, Shanghai Jiaotong University, Shanghai, China.
6
Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China.
7
Department of Ultrasonography, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
8
Department of Ultrasonography, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
9
Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.

Abstract

BACKGROUND:

A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. A novel diagnostic test that measures the expression of a 3-gene signature (DPP4, SCG5 and CA12) has demonstrated promise in thyroid carcinoma assessment. However, more reliable prediction methods combining clinical features with genomic signatures with high accuracy, good stability and low cost are needed.

METHODOLOGY/PRINCIPAL FINDINGS:

25 clinical information were recorded in 771 patients. Feature selection and validation were conducted using random forest. Thyroid samples and clinical data were obtained from 142 patients at two different hospitals, and expression of the 3-gene signature was measured using quantitative PCR. The predictive abilities of three models (based on the selected clinical variables, the gene expression profile, and integrated gene expression and clinical information) were compared. Seven clinical characteristics were selected based on a training set (539 patients) and tested in three test sets, yielding predictive accuracies of 82.3% (n = 232), 81.4% (n = 70), and 81.9% (n = 72). The predictive sensitivity, specificity, and accuracy were 72.3%, 80.5% and 76.8% for the model based on the gene expression signature, 66.2%, 81.8% and 74.6% for the model based on the clinical data, and 83.1%, 84.4% and 83.8% for the combined model in a 10-fold cross-validation (n = 142).

CONCLUSIONS:

These findings reveal that the integrated model, which combines clinical data with the 3-gene signature, is superior to models based on gene expression or clinical data alone. The integrated model appears to be a reliable tool for the preoperative diagnosis of thyroid tumors.

PMID:
27776138
PMCID:
PMC5077123
DOI:
10.1371/journal.pone.0164570
[Indexed for MEDLINE]
Free PMC Article

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