中华皮肤科杂志 ›› 2025, Vol. 58 ›› Issue (6): 523-529.doi: 10.35541/cjd.20250013

• 论著·荨麻疹 • 上一篇    下一篇

Olink靶向蛋白质组学技术分析慢性自发性荨麻疹患者抗组胺药治疗抵抗相关血清炎症因子

梁碧华    陈紫嫣    李华平    邹荟    林天一    李晓峰    张珞喻    李圣信    欧珊珊    陈教全    李润祥    朱慧兰   

  1. 广州医科大学皮肤病研究所  广州市皮肤病医院,广州  510095
  • 收稿日期:2025-01-10 修回日期:2025-05-08 发布日期:2025-06-03
  • 通讯作者: 朱慧兰 E-mail:zhlhuilan@126.com
  • 基金资助:
    广州市科技计划项目(2024A03J0479、2024A03J0559、2024A03J0424)

Analysis of serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria using the Olink-targeted proteomics technology

Liang Bihua, Chen Ziyan, Li Huaping, Zou Hui, Lin Tianyi, Li Xiaofeng, Zhang Luoyu, Li Shengxin, Ou Shanshan, Chen Jiaoquan, Li Runxiang, Zhu Huilan   

  1. Institute of Dermatology, Guangzhou Medical University, Guangzhou Dermatology Hospital, Guangzhou 510095, China
  • Received:2025-01-10 Revised:2025-05-08 Published:2025-06-03
  • Contact: Zhu Huilan E-mail:zhlhuilan@126.com
  • Supported by:
    Science and Technology Program of Guangzhou(2024A03J0479、2024A03J0559、2024A03J0424)

摘要: 【摘要】 目的 分析与慢性自发性荨麻疹(CSU)抗组胺药治疗抵抗相关的血清炎症因子。方法 收集2022年1月至2024年12月至广州市皮肤病医院就诊的88例CSU患者,采用《中国荨麻疹诊疗指南(2022版)》推荐的抗组胺药治疗,根据治疗4周后的7日荨麻疹活动度评分(UAS7)分为抗组胺药敏感组和抗组胺药抵抗组。采用Olink靶向蛋白质组学技术分析两组患者首次就诊时血清炎症因子水平,分析抗组胺药抵抗患者的特异性生物标志物,采用Spearman相关进行差异表达蛋白间的相关性分析。构建基于Olink蛋白质组数据的逻辑回归模型,采用受试者工作特征(ROC)曲线评估该模型的预测性能。计量资料采用x ± s或M(Q1,Q3)表示。结果 纳入88例CSU患者,年龄12 ~ 81(38.78 ± 13.89)岁,病程18(7.00,60.00)个月。敏感组32例,抵抗组56例,两组间年龄、病程、性别、过敏性疾病史等差异无统计学意义(均P > 0.05)。抗组胺药治疗4周后抵抗组患者UAS7评分[25.00(15.25,31.00)分]高于敏感组[0.50(0.00,4.00)分;Z = -7.08,P < 0.001]。Olink靶向蛋白质组分析筛选出两组间5个差异表达蛋白:与抗组胺药敏感组相比,抵抗组成纤维细胞生长因子19(FGF19)、白细胞介素15受体亚基α(IL-15RA)、嗜酸细胞活化趋化因子(CCL11)、单核细胞趋化蛋白1(MCP-1)蛋白表达水平均 > 2倍;敏感组磺基转移酶1A1(ST1A1)表达是抵抗组的2.54倍;差异表达蛋白中MCP-1特异性最高,其次为CCL11,分别为1.00和0.97。相关性分析显示,MCP-1和 CCL11呈显著正相关,IL-15RA和ST1A1呈显著负相关。采用ROC曲线评估差异蛋白对抗组胺药治疗抵抗预测的性能,MCP-1和CCL11的曲线下面积分别为0.603、0.630。结论 MCP-1和CCL11可能是预测CSU抗组胺药治疗抵抗的潜在生物标志物。

关键词: 慢性荨麻疹, 蛋白质组学, 组胺H1拮抗剂, 非镇静, 趋化因子CCL2, 趋化因子CCL11, 慢性自发性荨麻疹

Abstract: 【Abstract】 Objective To analyze serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria (CSU). Methods A total of 88 CSU patients were enrolled from Guangzhou Dermatology Hospital from January 2022 to December 2024. All patients received antihistamine treatment according to the “Guideline for diagnosis and treatment of urticaria in China (2022)”. Based on the 7-day urticaria activity score (UAS7) after 4-week treatment, these patients were divided into an antihistamine-sensitive group and an antihistamine-resistant group. Serum levels of inflammatory factors at the initial visit were analyzed using the Olink-targeted proteomics technology. Specific biomarkers associated with antihistamine resistance were identified, and Spearman correlation analysis was carried out to analyze correlations among differentially expressed proteins. A logistic regression model was constructed based on the Olink proteomics data, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Measurement data were expressed as mean ± standard deviation or median (lower quartile, upper quartile). Results The 88 CSU patients aged 12 to 81 (38.78 ± 13.89) years, with the disease duration being 18 (7.00, 60.00) months. There were 32 patients in the antihistamine-sensitive group and 56 in the antihistamine-resistant group. No significant differences were found between the two groups in terms of age, disease duration, gender, or history of allergic diseases (all P > 0.05). After 4 weeks of antihistamine treatment, the UAS7 score was significantly higher in the antihistamine-resistant group (25.00 [15.25, 31.00] points) than in the antihistamine-sensitive group (0.50 [0.00, 4.00] points; Z = -7.08, P < 0.001). The Olink-targeted proteomics identified 5 differentially expressed proteins between the two groups: compared with the antihistamine-sensitive group, the antihistamine-resistant group showed > 2-fold higher expression of fibroblast growth factor 19 (FGF19), interleukin-15 receptor subunit alpha (IL-15RA), eotaxin (CCL11), and monocyte chemoattractant protein-1 (MCP-1); in contrast, the expression of sulfotransferase 1A1 (ST1A1) in the antihistamine-sensitive group was 2.54 times that in the antihistamine-resistant group. Among the differentially expressed proteins, MCP-1 showed the highest specificity (1.00) for predicting antihistamine resistance, followed by CCL11 (0.97). Correlation analysis revealed a significant positive correlation between MCP-1 and CCL11, and a significant negative correlation between IL-15RA and ST1A1. ROC curve analysis showed that MCP-1 and CCL11 had area under the curve values of 0.603 and 0.630, respectively, in predicting antihistamine resistance. Conclusion MCP-1 and CCL11 may be potential biomarkers for predicting antihistamine resistance in CSU patients.

Key words: Chronic urticaria, Proteomics, Histamine H1 antagonists, non-sedating, Chemokine CCL2, Chemokine CCL11, Chronic spontaneous urticaria

引用本文

梁碧华 陈紫嫣 李华平 邹荟 林天一 李晓峰 张珞喻 李圣信 欧珊珊 陈教全 李润祥 朱慧兰. Olink靶向蛋白质组学技术分析慢性自发性荨麻疹患者抗组胺药治疗抵抗相关血清炎症因子[J]. 中华皮肤科杂志, 2025,58(6):523-529. doi:10.35541/cjd.20250013

Liang Bihua, Chen Ziyan, Li Huaping, Zou Hui, Lin Tianyi, Li Xiaofeng, Zhang Luoyu, Li Shengxin, Ou Shanshan, Chen Jiaoquan, Li Runxiang, Zhu Huilan. Analysis of serum inflammatory factors associated with antihistamine resistance in patients with chronic spontaneous urticaria using the Olink-targeted proteomics technology[J]. Chinese Journal of Dermatology, 2025, 58(6): 523-529.doi:10.35541/cjd.20250013