Becker M, Kist A M, Wendler O, Pesold V V, Bleier B S, Mueller S K
Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Bavaria, Germany.
Eur Rev Med Pharmacol Sci. 2025 Jan;29(1):1-11. doi: 10.26355/eurrev_202501_37054.
Chronic rhinosinusitis (CRS) has traditionally been classified phenotypically according to the presence (CRSwNP) or absence (CRSsNP) of nasal polyps. However, the phenotypic dichotomy does not represent the complexity of the disease. Current research thus focuses on identifying underlying inflammatory mechanisms and distinguishing different endotypes. The objectives of this study were 1) to identify maximally predictive non-invasive biomarkers from nasal mucus, 2) to apply machine learning algorithms to use mucus-derived biomarkers to classify phenotype, and 3) to determine the feature importance of each mucus biomarker to phenotypes.
This is an IRB-approved study of 103 CRS patients (37 CRSsNP, 66 CRSwNP). Nasal mucus was collected using merocele sponges after a 3-week steroid washout period. The nasal mucus was then examined for twelve cytokines/inflammatory protein biomarkers, including interferon (IFN)-γ, interleukin (IL)-4, -5, -17A, -22, immunoglobulin (Ig) E, cystatin-SA (CST-2), eosinophilic cationic protein (ECP), matrix metalloproteinase-9 (MMP-9), pappalysin-A (PAPP-A), periostin, and serpin E1. Protein concentrations were determined by ELISAs and Luminex assays. For phenotype classification, different artificial intelligence algorithms in increasing complexity, including t-distributed stochastic neighbor embedding (t-SNE), Adaboost, and XGBoost, were applied to the data from the biomarker analysis.
TThe analysis showed that IL-5 is a non-invasive marker to distinguish between the two phenotypic clusters. This was true for immune cell-derived proteins, and all proteins were analyzed conjointly. Periostin and CST-2 showed the highest feature importance for the epithelial- and tissue-derived proteins. The combination of IL-5, IgE, IL-17, and periostin showed the highest accuracy for prediction.
Nasal mucus can predict phenotypes similar to tissue, with IL-5 as the main trigger for clustering. Periostin and CST-2 may be part of important targetable pathways. Future efforts will be directed at determining how these markers may be used to guide therapeutic choices and individualize treatment.
慢性鼻-鼻窦炎（CRS）传统上根据是否存在鼻息肉（CRSwNP）进行表型分类，不存在鼻息肉的为CRSsNP。然而，这种表型二分法并不能体现该疾病的复杂性。因此，当前研究聚焦于确定潜在的炎症机制并区分不同的内型。本研究的目的是：1）从鼻黏液中识别出具有最大预测性的非侵入性生物标志物；2）应用机器学习算法，利用黏液衍生的生物标志物对表型进行分类；3）确定每种黏液生物标志物对表型的特征重要性。
这是一项经机构审查委员会（IRB）批准的研究，纳入了103例CRS患者（37例CRSsNP，66例CRSwNP）。在为期3周的类固醇洗脱期后，使用美罗囊肿海绵收集鼻黏液。随后检测鼻黏液中的12种细胞因子/炎症蛋白生物标志物，包括干扰素（IFN）-γ、白细胞介素（IL）-4、-5、-17A、-22、免疫球蛋白（Ig）E、胱抑素-SA（CST-2）、嗜酸性阳离子蛋白（ECP）、基质金属蛋白酶-9（MMP-9）、妊娠相关血浆蛋白-A（PAPP-A）、骨膜蛋白和丝氨酸蛋白酶抑制剂E1。通过酶联免疫吸附测定（ELISA）和Luminex检测法测定蛋白浓度。对于表型分类，将包括t分布随机邻域嵌入（t-SNE）、Adaboost和XGBoost等复杂度递增的不同人工智能算法应用于生物标志物分析的数据。
分析表明，IL-5是区分两个表型簇的非侵入性标志物。对于免疫细胞衍生蛋白以及对所有联合分析的蛋白而言都是如此。骨膜蛋白和CST-2对上皮和组织衍生蛋白显示出最高的特征重要性。IL-5、IgE、IL-17和骨膜蛋白的组合显示出最高的预测准确性。
鼻黏液能够预测与组织相似的表型，IL-5是聚类的主要触发因素。骨膜蛋白和CST-2可能是重要的可靶向通路的一部分。未来的工作将致力于确定如何利用这些标志物来指导治疗选择并实现个体化治疗。
