中华耳科学杂志2025，Vol.23Issue(5)：669-676,8.DOI:10.3969/j.issn.1672-2922.2025.05.009
通过蛋白质组学和机器学习发现血清的FCN-3和TMOD-4蛋白能够预测晕动病
Serum FCN-3 and TMOD-4 Can Predict Motion Sickness by Proteomics and Machine Learning
刘志 1张金红 1张纯 1汪晓宇 1李进让1
作者信息
- 1. 中国人民解放军总医院第六医学中心耳鼻咽喉头颈外科医学部咽喉嗓音外科(北京 100048)国家耳鼻咽喉疾病临床医学研究中心
- 折叠
摘要
Abstract
Objective To identify to explore proteins associated with motion sickness(MS)by proteomics techniques and machine learning.Methods Serum samples were collected from 51 patients with MS and 68 controls.Differentially expressed proteins(DEPs)were analyzed and discovered using proteomics techniques and bioinformatics methods,as well as predictive modeling using machine learning.Candidate proteins were validated by ELISA in a separate cohort.Results A total of 27 DEPs(11 up-regulated and 16 down-regulated)were identified to differ significantly between MS patients and controls.Functional enrichment analysis showed that DEPs were mainly enriched in platelet activation,ions binding,cellular exosomes,neurodegeneration,amyotrophic lateral sclerosis,Huntington's disease,immunity,and hemostasis.Based on multiple machine learning and ROC curve analyses,we constructed a potential diagnostic model based on the levels of the best 8 DEPs in serum with 95.8%specificity and 100%sensitivity(AUC=0.997,P<0.001).Among the eight DEPs,FCN-3 and TMOD-4,the two most abundant proteins,were selected as another potential model with 75.0%specificity and 91.7%sensitivity(AUC=0.870,P<0.001).ELISA results were consistent with proteomics analysis.Conclusion Our preliminary data suggest that MS and non-MS patients have different serum protein expression profiles.In addition,alterations in FCN-3 and TMOD-4 may serve as novel biomarkers for the diagnosis of MS.关键词
晕动病/蛋白质组学/FCN-3/TMOD-4/机器学习Key words
Motion sickness/proteomics/FCN-3/TMOD-4/machine learning引用本文复制引用
刘志,张金红,张纯,汪晓宇,李进让..通过蛋白质组学和机器学习发现血清的FCN-3和TMOD-4蛋白能够预测晕动病[J].中华耳科学杂志,2025,23(5):669-676,8.
