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基于 DNA 甲基化的方法区分甲状腺良恶性病变。

DNA Methylation-Based Method to Differentiate Malignant from Benign Thyroid Lesions.

机构信息

International Research Center - CIPE-A.C.Camargo Cancer Center, São Paulo, Brazil.

Faculty of Medicine, University of Sao Paulo State-UNESP, Botucatu, Brazil.

出版信息

Thyroid. 2019 Sep;29(9):1244-1254. doi: 10.1089/thy.2018.0458. Epub 2019 Aug 16.

DOI:10.1089/thy.2018.0458
PMID:31328658
Abstract

The differential diagnosis of thyroid nodules using fine-needle aspiration biopsy (FNAB) is challenging due to the inherent limitation of the cytology tests. The use of molecular markers has potential to complement the FNAB-based diagnosis and avoid unnecessary surgeries. In this study, we aimed to identify DNA methylation biomarkers and to develop a diagnostic tool useful for thyroid lesions. Genome-wide DNA methylation profiles (Illumina 450K) of papillary thyroid carcinoma (PTC = 60) and follicular thyroid carcinoma (FTC = 10) were compared with non-neoplastic thyroid tissue samples (NT = 50) and benign thyroid lesions (BTL = 17). The results were confirmed in publicly available databases from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) using the same DNA methylation platform. Two classifiers were trained to discriminate FTC and PTC from BTL. To increase the applicability of the method, six differentially methylated CpGs were selected and evaluated in 161 thyroid tumors and 69 BTL postsurgical specimens and 55 prospectively collected FNAB using bisulfite-pyrosequencing. DNA methylation analysis revealed 2130 and 19 differentially methylated CpGs in PTC and FTC, respectively. The CpGs confirmed by GEO and TCGA databases showing high areas under the receiver operating characteristic curve in all sample sets were used to train our diagnostic classifier. The model based on six CpGs was able to differentiate benign from malignant thyroid lesions with 94.3% sensitivity and 82.4% specificity. A similar performance was found applying the algorithm to TCGA and GEO external data sets (91.3-97.4% sensitivity and 87.5% specificity). We successfully evaluated the classifiers using a bisulfite-pyrosequencing technique, achieving 90.7% sensitivity and 75.4% specificity in surgical specimens (five of six CpGs). The study comprising FNAB cytology materials corroborated the applicability and performance of the methodology, demonstrating 86.7% sensitivity and 89.5% specificity in confirmed malignant tumors, and 100% sensitivity and 89% specificity in cases with indeterminate cytology. A novel diagnostic tool with potential application in preoperative screening of thyroid nodules is reported here. The proposed protocol has the potential to avoid unnecessary thyroidectomies.

摘要

甲状腺结节的细针穿刺活检(FNAB)的鉴别诊断具有挑战性,因为细胞学检查具有固有局限性。分子标志物的使用有可能补充 FNAB 诊断并避免不必要的手术。在这项研究中,我们旨在确定 DNA 甲基化生物标志物,并开发一种有用的甲状腺病变诊断工具。

我们比较了乳头状甲状腺癌(PTC = 60)和滤泡状甲状腺癌(FTC = 10)的全基因组 DNA 甲基化图谱(Illumina 450K)与非肿瘤性甲状腺组织样本(NT = 50)和良性甲状腺病变(BTL = 17)。使用相同的 DNA 甲基化平台,我们在来自基因表达综合数据库(GEO)和癌症基因组图谱(TCGA)的公共数据库中验证了结果。我们训练了两个分类器来区分 FTC 和 PTC 与 BTL。为了提高方法的适用性,我们选择了六个差异甲基化 CpG 并在 161 个甲状腺肿瘤和 69 个 BTL 术后标本和 55 个前瞻性收集的 FNAB 中使用亚硫酸氢盐-焦磷酸测序进行了评估。

DNA 甲基化分析显示 PTC 和 FTC 分别有 2130 个和 19 个差异甲基化 CpG。GEO 和 TCGA 数据库中经证实的 CpG 在所有样本组中具有较高的受试者工作特征曲线下面积,用于训练我们的诊断分类器。基于六个 CpG 的模型能够以 94.3%的灵敏度和 82.4%的特异性区分良性和恶性甲状腺病变。应用该算法到 TCGA 和 GEO 外部数据集时,发现具有相似的性能(91.3-97.4%的灵敏度和 87.5%的特异性)。我们使用亚硫酸氢盐-焦磷酸测序技术成功地评估了分类器,在手术标本中获得了 90.7%的灵敏度和 75.4%的特异性(五个 CpG 中有六个)。包括 FNAB 细胞学材料的研究证实了该方法的适用性和性能,在确认的恶性肿瘤中,该方法的灵敏度为 86.7%,特异性为 89.5%,在细胞学不确定的病例中,灵敏度为 100%,特异性为 89%。

这里报告了一种具有潜在应用于甲状腺结节术前筛查的新型诊断工具。所提出的方案有可能避免不必要的甲状腺切除术。

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