Article
作者: Chen, Mengni ; Luo, Dingcun ; Yao, Nan ; Gong, Tingting ; Lin, Xiangfeng ; Liu, Tong ; Chen, Wanyuan ; Xu, Dong ; Wei, Wenjun ; Kon, Oi Lian ; Han, Hong ; Peng, Li ; Yu, Jing ; Zheng, Haitao ; Xing, Michael Mingzhao ; Zheng, Shu ; Cai, Xue ; Sun, Yaoting ; Wu, Qijun ; Shen, Jiafei ; Qian, Liqin ; Iyer, N Gopalakrishna ; Hu, Pingping ; Selvarajan, Sathiyamoorthy ; Wu, Fan ; Zhou, Yan ; Guo, Qiaonan ; Zhao, Yongfu ; Alexander, Erik K ; Liu, Zhiyan ; Li, Lu ; Wang, He ; Guo, Tiannan ; Chen, Chuang ; Yuan, Zhennan ; Zhu, Yi ; Liu, Hanqing ; Wang, Jianbiao ; Zhang, Yifeng ; Ge, Minghua ; Du, Yuxin ; Gui, Zhiqiang ; Wang, Yingrui ; Zhang, Hao ; Zhu, Xin ; Pan, Jun ; Yang, Feng ; Chen, Ting ; Chen, Huanjie ; Zhu, Jingqiang ; Wu, Xiaohong ; Lv, Yangfan ; He, Yi ; Yao, Jincao ; Wang, Guangzhi ; Nie, Xiu ; Wei, Bei ; Wu, Yijun ; Wang, Yu ; Kakudo, Kennichi ; Guan, Haixia ; Sun, Wei ; Ge, Weigang ; Wang, Zhihong ; Wang, Jiatong
Abstract:Differentiating follicular thyroid adenoma (FTA) from carcinoma (FTC) remains challenging due to similar histological features separate from invasion. This study developed and validated DNA- and/or protein-based classifiers. A total of 2443 thyroid samples from 1568 patients were obtained from 24 centers in China and Singapore. Next-generation sequencing of a 66-gene panel revealed 41 (62.1%) detectable genes, while 25 were not, showing similar alteration patterns with differing mutation frequencies. Proteomics quantified 10,336 proteins, with 187 dysregulated. A discovery protein-based XGBoost model achieved an AUROC of 0.899 (95% CI, 0.849–0.949), outperforming the gene-based model (AUROC 0.670 [95% CI, 0.612–0.729]). A subsequent 24-protein classifier, developed via targeted mass spectrometry and validated in three independent sets, showed high performance in retrospective cohorts (AUROC 0.871 [95% CI, 0.833–0.910] and 0.853 [95% CI, 0.772–0.934]) and prospective biopsies (AUROC 0.781 [95% CI, 0.563–1.000]). It exhibited a 95.7% negative predictive value for ruling out malignancy. This study presents a promising protein-based approach for the differential diagnosis of FTA and FTC, potentially enhancing diagnostic accuracy and clinical decision-making.