Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology, 34141, Daejeon, Korea.
Graduate School of New Drug Discovery and Development, Chungnam National University, Daejeon, 34134, Korea.
Endocr Pathol. 2023 Sep;34(3):311-322. doi: 10.1007/s12022-023-09781-1. Epub 2023 Sep 2.
Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) is a low-risk thyroid tumor with a favorable prognosis. Nonetheless, differentiating NIFTP from other thyroid tumors remains challenging, necessitating reliable diagnostic markers. This study is aimed at discovering NIFTP-specific mRNA markers through RNA sequencing analysis of thyroid tumor tissues. We performed mRNA expression profiling for 74 fresh frozen thyroid tissue samples, including NIFTP and benign and malignant follicular-cell-derived tumors. NIFTP/malignant tumors showed 255 downregulated genes and 737 upregulated genes compared to benign tumors. Venn diagram analysis revealed 19 significantly upregulated and 7 downregulated mRNAs in NIFTP. Akaike information criterion analysis allowed us to select OCLN, ZNF423, LYG1, and AQP5 mRNA markers. We subsequently developed a predictive model based on logistic regression analysis using these four mRNAs, which we validated in independent samples (n = 90) using a qRT-PCR assay. This model demonstrated high accuracy in predicting NIFTP in discovery dataset (AUC (area under the receiver operating characteristic) = 0.960) and the validation dataset (AUC = 0.757). Our results suggest that OCLN, ZNF423, LYG1, and AQP5 mRNA markers might serve as reliable molecular markers for identifying NIFTP among other thyroid tumors, ultimately aiding in accurate diagnosis and management of NIFTP patients.
非浸润性滤泡甲状腺肿瘤伴乳头状核特征（NIFTP）是一种低风险的甲状腺肿瘤，具有良好的预后。尽管如此，将 NIFTP 与其他甲状腺肿瘤区分开来仍然具有挑战性，需要可靠的诊断标志物。本研究旨在通过对甲状腺肿瘤组织进行 RNA 测序分析来发现 NIFTP 特异性的 mRNA 标志物。我们对 74 个新鲜冷冻甲状腺组织样本进行了 mRNA 表达谱分析，包括 NIFTP 和良性及恶性滤泡细胞来源的肿瘤。与良性肿瘤相比，NIFTP/恶性肿瘤显示出 255 个下调基因和 737 个上调基因。Venn 图分析显示 NIFTP 中有 19 个显著上调和 7 个下调的 mRNAs。Akaike 信息准则分析允许我们选择 OCLN、ZNF423、LYG1 和 AQP5 mRNA 标志物。我们随后使用这些四个 mRNA 基于逻辑回归分析开发了一个预测模型，并使用 qRT-PCR 检测在独立样本（n=90）中进行了验证。该模型在发现数据集（AUC（接受者操作特征曲线下的面积）=0.960）和验证数据集（AUC=0.757）中均能准确预测 NIFTP。我们的结果表明，OCLN、ZNF423、LYG1 和 AQP5 mRNA 标志物可能作为识别其他甲状腺肿瘤中 NIFTP 的可靠分子标志物，最终有助于 NIFTP 患者的准确诊断和管理。
