一个基于蛋白质的分类器,用于区分卵泡甲状腺瘤和甲状腺癌
Yaoting Sun1,2,3, He Wang1,2,3, Lu Li1,2,3,4
1Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, No. 18 Shilongshan Road, Hangzhou, 310024, China.
EMBO molecular medicine
|May 29, 2025
概括
一个新的基于蛋白质的测试可以准确地区分卵泡甲状腺瘤和甲状腺癌,改善诊断. 这种方法显示出高准确性和强大的负预测值,有助于在甲状腺癌诊断中的临床决策.
科学领域:
- 内分泌学和瘤学 在内分泌学和瘤学.
- 分子诊断学 分子诊断
- 蛋白质组学和基因组学
背景情况:
- 鉴别卵泡性甲状腺瘤 (FTA) 和卵泡性甲状腺癌 (FTC) 是由于重叠的特征,特别是没有囊入侵,在组织学上具有挑战性.
- 准确的区分对于适当的患者管理和治疗策略在甲状腺结节评估中至关重要.
研究的目的:
- 开发和验证基于DNA和/或蛋白质的新型诊断分类器,以区分FTA和FTC.
- 评估这些分类器的诊断性能,特别关注基于蛋白质的方法.
主要方法:
- 分析了来自中国和新加坡24个中心1568名患者的2443个甲状腺样本.
- 66个基因组的下一代测序和定量蛋白质组学以确定分子变化.
- 使用基因和蛋白质表达数据开发和验证机器学习模型 (XGBoost),包括针对蛋白质分类器的向质谱.
主要成果:
- 蛋白质组学发现了187种失调的蛋白质,而基因测序检测到66个基因中的41个基因发生了变化.
- 一个基于蛋白质的XGBoost模型实现了0.899的接收器操作特征曲线 (AUROC) 下的面积,显著超过基因模型 (AUROC 0.670).
- 一个经过验证的24种蛋白质分类器在回顾性 (AUROC 0.871,0.853) 和前性 (AUROC 0.781) 设置中显示出高性能,在排除恶性瘤方面具有95.7%的负预测值.
结论:
- 一个基于蛋白质的诊断分类器显示出对卵泡性甲状腺瘤和甲状腺癌的准确差异诊断具有显著的希望.
- 这种方法有可能提高诊断准确度,减少错误分类,并改善甲状腺结节评估中的临床决策.
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