Wei Siyu, Yue Zehong, Sun Chen, Zou Yuping, Chen Haiyan, Tao Junxian, Xu Jing, Xu Yuan, Wang Ning, Guo Yan, Ren Qinduo, Wang Chang, Lu Songlin, Ma Ye, Dong Yu, Zhang Chen, Sun Hongmei, Tang Guoping, Kong Fanwu, Lv Wenhua, Shang Zhenwei, Zhang Mingming, Jiang Yongshuai, Lyu Hongchao
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Department of Medical Engineering, the Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, China.
Front Immunol. 2025 Jul 15;16:1618805. doi: 10.3389/fimmu.2025.1618805. eCollection 2025.
Psoriasis is a chronic immune-mediated inflammatory skin disease with a significant global burden. Current risk assessment lacks integration of proteomic data with genetic and clinical factors. This study aimed to develop a plasma proteomics-based risk score (ProtRS) to improve psoriasis prediction.
Using data from 53,065 UK Biobank (UKB) participants (1,122 psoriasis cases; 51,943 controls), we integrated 2,923 plasma proteins, polygenic risk score (PRS), and seven clinical risk factors. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm with 10-fold cross-validation identified stable proteins for ProtRS construction. Population Attributable Fractions (PAFs) for risk factors were calculated.
LASSO regression identified 26 highly stable proteins forming ProtRS-26. ProtRS-26 significantly outperformed PRS and clinical risk factors alone. Combining ProtRS-26 with PRS and clinical factors further improved prediction. Key proteins were enriched in pro-inflammatory pathways and skin-derived. PAF analysis identified hypertension and obesity as major modifiable risk factors.
Plasma proteomics significantly enhances psoriasis risk prediction compared to genetic and clinical factors alone. ProtRS-26 provides a robust tool for early screening and personalized prevention.
银屑病是一种慢性免疫介导的炎症性皮肤病，在全球造成重大负担。目前的风险评估缺乏将蛋白质组学数据与遗传和临床因素相结合。本研究旨在开发一种基于血浆蛋白质组学的风险评分（ProtRS），以改善银屑病预测。
利用来自53065名英国生物银行（UKB）参与者（1122例银屑病病例；51943例对照）的数据，我们整合了2923种血浆蛋白、多基因风险评分（PRS）和七个临床风险因素。采用具有10倍交叉验证的最小绝对收缩和选择算子（LASSO）算法确定用于构建ProtRS的稳定蛋白。计算风险因素的人群归因分数（PAF）。
LASSO回归确定了26种高度稳定的蛋白，形成ProtRS-26。ProtRS-26显著优于单独的PRS和临床风险因素。将ProtRS-26与PRS和临床因素相结合进一步改善了预测。关键蛋白在促炎途径和皮肤来源中富集。PAF分析确定高血压和肥胖是主要的可改变风险因素。
与单独的遗传和临床因素相比，血浆蛋白质组学显著提高了银屑病风险预测。ProtRS-26为早期筛查和个性化预防提供了一个强大的工具。
