由于细针穿刺活检的细胞学检查不确定，因此甲状腺恶性肿瘤的可靠术前诊断仍然具有挑战性。尽管许多研究已经成功证明了高通量分子诊断在癌症预测中的应用，微阵列在常规临床应用中的应用仍然有限.我们的目标是,因此,识别一小部分基因，以开发一种实用且廉价的临床诊断工具。我们开发了一种两步特征选择方法，该方法由微阵列数据的线性模型（LIMMA）线性模型和迭代贝叶斯模型平均模型组成，以识别合适的基因集签名。使用一个公共数据集进行训练，我们发现了三基因标记二肽基肽酶4（DPP4），分泌颗粒蛋白V（SCG5）和碳酸酐酶XII（CA12）。然后，我们使用其他三个独立的公共数据集评估了我们的基因集的稳健性。基因签名准确率分别为85.7、78.8和85.7％，分别。对于实验验证，我们从手术中收集了70份甲状腺样本,我们的三基因签名方法通过定量聚合酶链反应(QPCR)实验获得了94.3%的准确率.此外，在29个样本中的免疫组织化学显示，这三个基因表达的蛋白质在甲状腺样本中也有差异表达。我们的方案发现了一个强大的三基因签名，可以区分良性和恶性甲状腺肿瘤，将有日常的临床应用。
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.
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