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A three‐gene panel that distinguishes benign from malignant thyroid nodules
Author(s) -
Zheng Bing,
Liu Jun,
Gu Jianlei,
Lu Yao,
Zhang Wei,
Li Min,
Lu Hui
Publication year - 2014
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.29172
Subject(s) - thyroid cancer , microarray , computational biology , thyroid nodules , microarray analysis techniques , dna microarray , gene signature , gene , feature selection , thyroid , biology , pathology , bioinformatics , medicine , computer science , gene expression , artificial intelligence , genetics
Reliable preoperative diagnosis of malignant thyroid tumors remains challenging because of the inconclusive cytological examination of fine‐needle aspiration biopsies. Although numerous studies have successfully demonstrated the use of high‐throughput molecular diagnostics in cancer prediction, the application of microarrays in routine clinical use remains limited. Our aim was, therefore, to identify a small subset of genes to develop a practical and inexpensive diagnostic tool for clinical use. We developed a two‐step feature selection method composed of a linear models for microarray data (LIMMA) linear model and an iterative Bayesian model averaging model to identify a suitable gene set signature. Using one public dataset for training, we discovered a three‐gene signature dipeptidyl‐ peptidase 4 (DPP4), secretogranin V (SCG5) and carbonic anhydrase XII (CA12). We then evaluated the robustness of our gene set using three other independent public datasets. The gene signature accuracy was 85.7, 78.8 and 85.7%, respectively. For experimental validation, we collected 70 thyroid samples from surgery and our three‐gene signature method achieved an accuracy of 94.3% by quantitative polymerase chain reaction (QPCR) experiment. Furthermore, immunohistochemistry in 29 samples showed proteins expressed by these three genes are also differentially expressed in thyroid samples. Our protocol discovered a robust three‐gene signature that can distinguish benign from malignant thyroid tumors, which will have daily clinical application.