ORIGINAL RESEARCH article
Front. Immunol.
Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1618805
This article is part of the Research TopicAdvancing the Understanding and Management of Immune Skin Conditions Through Multiomic ApproachesView all articles
Plasma Proteomics-Based Risk Scores for Psoriasis Prediction: A Novel Approach to Early Diagnosis
Provisionally accepted
Siyu Wei1
Zehong Yue1Chen Sun1Yuping Zou1Haiyan Chen1Junxian Tao1Jing Xu1Yuan Xu1Ning Wang1Yan Guo1Qinduo Ren1Chang Wang1Songlin Lu1Ye Ma1Yu Dong1Chen Zhang1Hongmei Sun1
Tang Guoping Guoping2Fanwu Kong3
Wenhua Lv1Zhenwei Shang1
Mingming Zhang1Hongchao Lyu1
Yongshuai Jiang1*- 1Harbin Medical University, Harbin, China
- 2Fourth Affiliated Hospital, School of Medicine, Zhejiang University, Yiwu, Zhejiang Province, China
- 3Department of Nephrology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China
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Psoriasis is a chronic immune-mediated inflammatory skin disease with a complex etiology and high global burden. Currently, there is no research that emphasizes integrating proteomics data with genetic and clinical factors to improve the ability to assess psoriasis risk. In this study, we explored the potential of plasma proteomics in predicting psoriasis risk using data of 53,065 participants from UK Biobank (UKB).We integrated 2,923 plasma protein measurements, polygenic risk score (PRS) and seven clinical risk factors into a machine learning framework. Using the least absolute shrinkage and selection operator (LASSO) algorithm, we identified 26 highly stable proteins and constructed the protein risk score (ProtRS). The predictive accuracy of ProtRS-26 (AUC: 0.7809) was significantly higher compared to PRS (AUC: 0.5385) and clinical risk factors (AUC: 0.5475). Combining ProtRS-26 with PRS and clinical risk factors further improved model efficacy (AUC: 0.7986). Functional analysis revealed key proteins, such as IL-36G and IL-22, involved in psoriasis pathogenesis through pro-inflammatory cytokine networks. In addition, population attributable fraction (PAF) analysis emphasized hypertension and obesity as major risk factors.Our findings suggest that plasma proteomics-based risk score significantly improves the risk prediction of psoriasis and provides valuable insights for early screening, prevention and personalized treatment.
Keywords: Psoriasis, Plasma proteomics, LASSO, protein risk score model, population attributable fraction
Received: 27 Apr 2025; Accepted: 16 Jun 2025.
Copyright: © 2025 Wei, Yue, Sun, Zou, Chen, Tao, Xu, Xu, Wang, Guo, Ren, Wang, Lu, Ma, Dong, Zhang, Sun, Guoping, Kong, Lv, Shang, Zhang, Lyu and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yongshuai Jiang, Harbin Medical University, Harbin, China
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