Yu Qingxiang, Hao Weijing, He Yanbin, Ruan Xianhui, Liu Lin, Yun Xinwei, Li Dapeng, Zhao Jingzhu, Cao Wenfeng, Yin Yu, Hu Linfei, Qin Xuan, Gao Ming, Zhang Lei, Zheng Xiangqian
Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co., Ltd., Hangzhou, Zhejiang, China.
Department of Thyroid and Neck Tumor, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Endocrine. 2025 Jun 15. doi: 10.1007/s12020-025-04308-6.
Lateral lymph node metastasis (LNM) critically influences surgical decision-making in papillary thyroid carcinoma (PTC). However, the sensitivity of preoperative imageological examination in detecting LNM remains suboptimal, necessitating the development of more accurate diagnostic and predictive tools. This study aims to identify multi-omics biomarkers and construct a predictive model for LNM.
We performed a comprehensive multi-omics analysis of 50 PTCs presenting with (LNM group) or without lateral lymph node metastases (LNN group) using whole exome sequencing and whole transcriptome sequencing.
Younger age, larger tumor size, and lymphovascular invasion were associated with increased risk of LNM, while invasive follicular subtype was associated with lower risk of LNM. Genomic landscape analysis identified 23 LNM group specific driver mutations and 15 protective variants in the LNN group. Transcriptome analysis identified 444 differentially expressed genes associated with LNM. Weighted gene co-expression network analysis revealed a module that correlated negatively with LNM, with key genes significantly enriched in Notch signaling pathway and Apelin signaling pathway. Notably, elevated neutrophils in tumor immune microenvironment was strongly associated with high LNM risk, suggesting neutrophils as potential early predictors of lateral lymph node metastasis in PTC. A machine learning-based multi-gene classifier was developed to predict LNM, achieving excellent performance with an area under the curve (AUC) of 0.98 in the training set and 0.892 in the test set.
This study provides novel insights into the molecular characteristics of PTC associated with lateral lymph node metastasis, highlighting tumor-infiltrating neutrophils as an independent LNM predictor. The multi-gene classifier developed in this study demonstrates promising clinical utility for improving the accuracy of LNM prediction and guiding personalized treatment strategies in PTC.
侧方淋巴结转移（LNM）对甲状腺乳头状癌（PTC）的手术决策有至关重要的影响。然而，术前影像学检查在检测LNM方面的敏感性仍不理想，因此需要开发更准确的诊断和预测工具。本研究旨在识别多组学生物标志物并构建LNM的预测模型。
我们对50例伴有（LNM组）或不伴有侧方淋巴结转移（LNN组）的PTC进行了全面的多组学分析，采用全外显子组测序和全转录组测序。
年龄较小、肿瘤较大和淋巴血管侵犯与LNM风险增加有关，而侵袭性滤泡亚型与LNM风险较低有关。基因组景观分析确定了LNM组23个特定的驱动突变和LNN组15个保护性变异。转录组分析确定了444个与LNM相关的差异表达基因。加权基因共表达网络分析揭示了一个与LNM呈负相关的模块，关键基因在Notch信号通路和Apelin信号通路中显著富集。值得注意的是，肿瘤免疫微环境中中性粒细胞升高与高LNM风险密切相关，提示中性粒细胞是PTC侧方淋巴结转移的潜在早期预测指标。开发了一种基于机器学习的多基因分类器来预测LNM，在训练集中曲线下面积（AUC）为0.98，在测试集中为0.892，表现优异。
本研究为PTC与侧方淋巴结转移相关的分子特征提供了新的见解，突出肿瘤浸润性中性粒细胞作为独立的LNM预测指标。本研究开发的多基因分类器在提高LNM预测准确性和指导PTC个性化治疗策略方面显示出有前景 的临床应用价值。
