Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis
IntroductionPsoriasis 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...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1618805/full |
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| author | Siyu Wei Zehong Yue Chen Sun Yuping Zou Haiyan Chen Junxian Tao Jing Xu Yuan Xu Ning Wang Yan Guo Qinduo Ren Chang Wang Songlin Lu Ye Ma Yu Dong Chen Zhang Hongmei Sun Guoping Tang Fanwu Kong Wenhua Lv Zhenwei Shang Mingming Zhang Yongshuai Jiang Hongchao Lyu |
| author_facet | Siyu Wei Zehong Yue Chen Sun Yuping Zou Haiyan Chen Junxian Tao Jing Xu Yuan Xu Ning Wang Yan Guo Qinduo Ren Chang Wang Songlin Lu Ye Ma Yu Dong Chen Zhang Hongmei Sun Guoping Tang Fanwu Kong Wenhua Lv Zhenwei Shang Mingming Zhang Yongshuai Jiang Hongchao Lyu |
| author_sort | Siyu Wei |
| collection | DOAJ |
| description | IntroductionPsoriasis 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.MethodsUsing 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.ResultsLASSO 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.DiscussionPlasma 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. |
| format | Article |
| id | doaj-art-c61c19ea082d4d609b4d58b803fa444a |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-c61c19ea082d4d609b4d58b803fa444a2025-08-20T03:50:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-07-011610.3389/fimmu.2025.16188051618805Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosisSiyu Wei0Zehong Yue1Chen Sun2Yuping Zou3Haiyan Chen4Junxian Tao5Jing Xu6Yuan Xu7Ning Wang8Yan Guo9Qinduo Ren10Chang Wang11Songlin Lu12Ye Ma13Yu Dong14Chen Zhang15Hongmei Sun16Guoping Tang17Fanwu Kong18Wenhua Lv19Zhenwei Shang20Mingming Zhang21Yongshuai Jiang22Hongchao Lyu23College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaDepartment of Medical Engineering, the Fourth Affiliated Hospital of School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, ChinaDepartment of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, ChinaIntroductionPsoriasis 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.MethodsUsing 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.ResultsLASSO 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.DiscussionPlasma 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1618805/fullpsoriasisplasma proteomicsLASSOprotein risk score modelpopulation attributable fraction |
| spellingShingle | Siyu Wei Zehong Yue Chen Sun Yuping Zou Haiyan Chen Junxian Tao Jing Xu Yuan Xu Ning Wang Yan Guo Qinduo Ren Chang Wang Songlin Lu Ye Ma Yu Dong Chen Zhang Hongmei Sun Guoping Tang Fanwu Kong Wenhua Lv Zhenwei Shang Mingming Zhang Yongshuai Jiang Hongchao Lyu Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis Frontiers in Immunology psoriasis plasma proteomics LASSO protein risk score model population attributable fraction |
| title | Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis |
| title_full | Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis |
| title_fullStr | Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis |
| title_full_unstemmed | Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis |
| title_short | Plasma proteomics-based risk scores for psoriasis prediction: a novel approach to early diagnosis |
| title_sort | plasma proteomics based risk scores for psoriasis prediction a novel approach to early diagnosis |
| topic | psoriasis plasma proteomics LASSO protein risk score model population attributable fraction |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1618805/full |
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