鼻液和血清的蛋白质分析,用于诊断2型鼻息肉
Yilin Hou1, Changhui Chen2, Zhengqi Li2
1Department of Otorhinolaryngology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China; Department of Allergy, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China; Otorhinolaryngology Institute of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China; Guangzhou Key Laboratory of Otorhinolaryngology, Guangzhou, Guangdong, P.R. China; Otorhinolaryngology, Peking University Shenzhen Hospital, Shenzhen Peking University-the Hong Kong University of Science and Technology Medical Center, Shenzhen, China.
机器学习确定了鼻液中的四种关键蛋白质,以预测2型慢性鼻炎与鼻息肉 (T2 CRSwNP). 这种非侵入性蛋白质组方法有助于诊断T2 CRSwNP并了解复发.
科学领域:
- 发现生物标志物的发现.
- 蛋白质组学是指蛋白质组学.
- 机器学习在医学中的应用
背景情况:
- 2型慢性鼻炎与鼻息肉 (T2 CRSwNP) 呈现严重的症状和频繁的复发.
- 对于T2 CRSwNP诊断的非侵入性生物标志物尚未得到充分研究.
研究的目的:
- 评估鼻液和血清蛋白质表达对T2 CRSwNP的预测值.
- 开发一种用于使用非侵入性生物标志物的T2 CRSwNP诊断的机器学习模型.
主要方法:
- 使用集群分析对T2和非T2CRSwNP患者进行分类.
- 通过近距离延伸测定,通过鼻液和血清测量了92种与炎症相关的蛋白质.
- 开发了一个使用机器学习进行5倍交叉验证的诊断模型.
主要成果:
- 在鼻液中确定了23种失调的蛋白质,在血清中16种.
- 从鼻液中选择了四种生物标志物 (GDNF,MCP-4,TGFB1,CST5) 来进行T2 CRSwNP预测.
- 使用开发的模型实现了高预测精度 (AUC 0.91-0.92). 确定了GDNF和MCP-4作为独立的预后生物标志物.
结论:
- 蛋白质组分析和机器学习有效地确定了T2 CRSwNP中的炎症内型和复发模式.
- 这项研究提供了一种简单的,非侵入性的方法来诊断T2 CRSwNP.
- 鼻液生物标志物显示T2 CRSwNP诊断和预后的显著潜力.
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