Wang Qing-Xuan, Chen En-Dong, Cai Ye-Feng, Li Quan, Jin Yi-Xiang, Jin Wen-Xu, Wang Ying-Hao, Zheng Zhou-Ci, Xue Lu, Wang Ou-Chen, Zhang Xiao-Hua
Department of Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, 325000, China.
Department of Otolaryngology Head and Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University, School of Medicine, Shanghai, 200000, China.
J Exp Clin Cancer Res. 2016 Oct 28;35(1):169. doi: 10.1186/s13046-016-0447-3.
Clinicians are confronted with an increasing number of patients with thyroid nodules. Reliable preoperative diagnosis of thyroid nodules remains a challenge because of inconclusive cytological examination of fine-needle aspiration biopsies. Although molecular analysis of thyroid tissue has shown promise as a diagnostic tool in recent years, it has not been successfully applied in routine clinical use, particularly in Chinese patients.
Whole-transcriptome sequencing of 19 primary papillary thyroid cancer (PTC) samples and matched adjacent normal thyroid tissue (NT) samples were performed. Bioinformatics analysis was carried out to identify candidate diagnostic genes. Then, RT-qPCR was performed to evaluate these candidate genes, and four genes were finally selected. Based on these four genes, diagnostic algorithm was developed (training set: 100 thyroid cancer (TC) and 65 benign thyroid lesions (BTL)) and validated (independent set: 123 TC and 81 BTL) using the support vector machine (SVM) approach.
We discovered four genes, namely fibronectin 1 (FN1), gamma-aminobutyric acid type A receptor beta 2 subunit (GABRB2), neuronal guanine nucleotide exchange factor (NGEF) and high-mobility group AT-hook 2 (HMGA2). A SVM model with these four genes performed with 97.0 % sensitivity, 93.8 % specificity, 96.0 % positive predictive value (PPV), and 95.3 % negative predictive value (NPV) in training set. For additional independent validation, it also showed good performance (92.7 % sensitivity, 90.1 % specificity, 93.4 % PPV, and 89.0 % NPV).
Our diagnostic panel can accurately distinguish benign from malignant thyroid nodules using a simple and affordable method, which may have daily clinical application in the near future.
临床医生面临的甲状腺结节患者数量日益增多。由于细针穿刺活检的细胞学检查结果不明确，甲状腺结节的可靠术前诊断仍然是一项挑战。尽管近年来甲状腺组织的分子分析作为一种诊断工具显示出了前景，但尚未成功应用于常规临床实践，尤其是在中国患者中。
对19例原发性乳头状甲状腺癌（PTC）样本及匹配的相邻正常甲状腺组织（NT）样本进行全转录组测序。进行生物信息学分析以鉴定候选诊断基因。然后，进行逆转录定量聚合酶链反应（RT-qPCR）以评估这些候选基因，最终选择了四个基因。基于这四个基因，开发了诊断算法（训练集：100例甲状腺癌（TC）和65例良性甲状腺病变（BTL）），并使用支持向量机（SVM）方法进行了验证（独立集：123例TC和81例BTL）。
我们发现了四个基因，即纤连蛋白1（FN1）、γ-氨基丁酸A型受体β2亚基（GABRB2）、神经元鸟嘌呤核苷酸交换因子（NGEF）和高迁移率族AT钩蛋白2（HMGA2）。在训练集中，包含这四个基因的SVM模型的灵敏度为97.0%，特异度为93.8%，阳性预测值（PPV）为96.0%，阴性预测值（NPV）为95.3%。对于额外的独立验证，该模型也表现出良好的性能（灵敏度为92.7%，特异度为90.1%，PPV为93.4%，NPV为89.0%）。
我们的诊断组可以使用一种简单且经济实惠的方法准确区分甲状腺结节的良恶性，这在不久的将来可能会在日常临床中得到应用。
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