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• W1604861791 abstract "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." @default.
• W1604861791 created "2016-06-24" @default.
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• W1604861791 date "2014-09-22" @default.
• W1604861791 modified "2023-10-16" @default.
• W1604861791 title "A three‐gene panel that distinguishes benign from malignant thyroid nodules" @default.
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• W1604861791 doi "https://doi.org/10.1002/ijc.29172" @default.
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