Nguyen Truong Phan-Xuan, Le Minh-Khang, Roytrakul Sittiruk, Shuangshoti Shanop, Kitkumthorn Nakarin, Keelawat Somboon
Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Department of Pathology, University of Yamanashi, Chuo City, Japan.
J Pathol Transl Med. 2025 Jan;59(1):39-49. doi: 10.4132/jptm.2024.09.14. Epub 2024 Oct 24.
Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.
We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.
We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC >0.5.
We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.
尽管滤泡型甲状腺肿瘤的诊断标准已明确，但在某些情况下诊断这些病变仍具有挑战性。在世界卫生组织最新的《内分泌和神经内分泌肿瘤分类》（第5版）中，侵袭性包裹性乳头状甲状腺癌滤泡变体被重新分类为独立的实体。由于其形态学特征相似，将这种乳头状甲状腺癌变体与低风险滤泡型肿瘤区分开来至关重要。蛋白质组学在检测和定量蛋白质生物标志物方面具有巨大潜力。我们最初使用福尔马林固定石蜡包埋样本，研究了由机器学习定义的蛋白质生物标志物组在识别侵袭性包裹性乳头状甲状腺癌变体方面的潜在价值。
我们开发了一种监督式机器学习模型，并使用来自46个甲状腺组织样本的蛋白质组学数据测试其性能。
我们应用了一种随机森林分类器，该分类器利用五种蛋白质生物标志物（ZEB1、NUP98、C2C2L、NPAP1和KCNJ3）。在区分侵袭性包裹性乳头状甲状腺癌变体与非恶性样本的训练样本中，该分类器的曲线下面积（AUC）为1.00，准确率为1.00。此外，我们分析了单蛋白/基因接收器操作特征在癌症基因组图谱项目中区分侵袭性包裹性乳头状甲状腺癌变体与其他变体的性能，其AUC>0.5。
我们证明了高通量蛋白质组学与机器学习相结合能够有效区分侵袭性包裹性乳头状甲状腺癌变体与其他滤泡型甲状腺肿瘤。
