Nguyen Truong Phan-Xuan, Nguyen Hoang Minh, Luu Loi Phuc, Ngo Dat Quoc, Shuangshoti Shanop, Kitkumthorn Nakarin, Keelawat Somboon
Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand; Department of Pathology, Faculty of Medicine, University of Medicine & Pharmacy HCMC, Ho Chi Minh City 70000, Vietnam.
Faculty of Biological Sciences, Nong Lam University Ho Chi Minh City, Ho Chi Minh City 70000, Vietnam.
Pathol Res Pract. 2025 Feb;266:155794. doi: 10.1016/j.prp.2024.155794. Epub 2024 Dec 31.
Follicular-patterned thyroid tumors (FPTTs) are frequently encountered in thyroid pathology, encompassing follicular adenoma (FA), follicular thyroid carcinoma (FTC), noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP), and follicular variant of papillary thyroid carcinoma (fvPTC). Recently, a distinct entity termed differentiated high-grade thyroid carcinoma has been described by the 5th edition of the WHO classification of the thyroid tumors, categorized as either high-grade fvPTC, high-grade FTC or high-grade oncocytic carcinoma of the thyroid (OCA). Accurate differentiation among these lesions, particular between the benign (FA), borderline (NIFTP) and malignant neoplasms (FTC and fvPTC), remains a challenge in both histopathological and cytological diagnoses. This study aimed to develop a novel molecular diagnostic approach utilizing DNA methylation to distinguish between these thyroid tumors.
DNA methylation signatures and machine learning were employed to construct classification models for FPTTs. A total of 178 thyroid samples from the Gene Expression Omnibus were analyzed. The models were validated using two independent cohorts.
13 cytosine-guanine dinucleotides (CpGs) exhibited significant differences in methylation levels among FA, FTC, NIFTP and fvPTC. Notably, NIFTP showed hypomethylation compared to other subtypes. A Random Forest classifier, based on the methylation status of these 13 CpGs, effectively categorized the four tumor subtypes (AUC = 0.86, accuracy = 0.70 for internal data, and AUC approximately 0.80 for validation data). The selected CpGs were significantly associated with the tumor progression pathway.
This study established a robust method for categorizing FPTTs based on DNA methylation patterns. The identified DNA methylation approach holds clinical promise for efficiently diagnosing thyroid neoplasms.
滤泡型甲状腺肿瘤（FPTTs）在甲状腺病理学中较为常见，包括滤泡性腺瘤（FA）、滤泡状甲状腺癌（FTC）、具有乳头样核特征的非侵袭性滤泡性甲状腺肿瘤（NIFTP）以及甲状腺乳头状癌滤泡变体（fvPTC）。最近，世界卫生组织甲状腺肿瘤分类第5版描述了一种名为分化型高级别甲状腺癌的独特实体，分为高级别fvPTC、高级别FTC或高级别甲状腺嗜酸细胞癌（OCA）。准确区分这些病变，尤其是良性（FA）、交界性（NIFTP）和恶性肿瘤（FTC和fvPTC），在组织病理学和细胞学诊断中仍然是一项挑战。本研究旨在开发一种利用DNA甲基化来区分这些甲状腺肿瘤的新型分子诊断方法。
采用DNA甲基化特征和机器学习构建FPTTs的分类模型。对来自基因表达综合数据库的178份甲状腺样本进行了分析。使用两个独立队列对模型进行验证。
13个胞嘧啶-鸟嘌呤二核苷酸（CpG）在FA、FTC、NIFTP和fvPTC之间的甲基化水平存在显著差异。值得注意的是，与其他亚型相比，NIFTP表现出低甲基化。基于这13个CpG的甲基化状态的随机森林分类器有效地对四种肿瘤亚型进行了分类（内部数据的AUC = 0.86，准确率 = 0.70，验证数据的AUC约为0.80）。所选的CpG与肿瘤进展途径显著相关。
本研究建立了一种基于DNA甲基化模式对FPTTs进行分类的可靠方法。所确定的DNA甲基化方法在高效诊断甲状腺肿瘤方面具有临床应用前景。
