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<p>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.</p>

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A protein-based classifier for differentiating follicular thyroid adenoma and carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月29日

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  • Yaoting Sun,
  • He Wang,
  • Lu Li,
  • Jianbiao Wang,
  • Wanyuan Chen,
  • Li Peng,
  • Pingping Hu,
  • Jing Yu,
  • Xue Cai,
  • Nan Yao,
  • Yan Zhou,
  • Jiatong Wang,
  • Yingrui Wang,
  • Liqin Qian,
  • Weigang Ge,
  • Mengni Chen,
  • Feng Yang,
  • Zhiqiang Gui,
  • Wei Sun,
  • Zhihong Wang,
  • Minghua Ge,
  • Yi He,
  • Guangzhi Wang,
  • Yongfu Zhao,
  • Huanjie Chen,
  • Xiaohong Wu,
  • Yuxin Du,
  • Wenjun Wei,
  • Fan Wu,
  • Dingcun Luo,
  • Xiangfeng Lin,
  • Haitao Zheng,
  • Xin Zhu,
  • Bei Wei,
  • Jiafei Shen,
  • Jincao Yao,
  • Zhennan Yuan,
  • Tong Liu,
  • Jun Pan,
  • Yifeng Zhang,
  • Yangfan Lv,
  • Qiaonan Guo,
  • Qijun Wu,
  • Tingting Gong,
  • Ting Chen,
  • Shu Zheng,
  • Jingqiang Zhu,
  • Hanqing Liu,
  • Chuang Chen,
  • Hong Han,
  • Sathiyamoorthy Selvarajan,
  • Michael Mingzhao Xing,
  • Kennichi Kakudo,
  • Erik K Alexander,
  • Yijun Wu,
  • Yu Wang,
  • Dong Xu,
  • Hao Zhang,
  • Xiu Nie,
  • Oi Lian Kon,
  • N Gopalakrishna Iyer,
  • Zhiyan Liu,
  • Yi Zhu,
  • Haixia Guan,
  • Tiannan Guo

摘要

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.