ArticlePsoriasis (Auckland, N.Z.)2024
Unveiling Novel Protein Biomarkers for Psoriasis Through Integrated Analysis of Human Plasma Proteomics and Mendelian Randomization.
Article in Psoriasis (Auckland, N.Z.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Integrated multi-omics strategies for identifying novel therapies in psoriasis.Bioinformatics (Oxford, England) · 2026Article
- Multi‑omics and their integration in psoriasis research (Review).Molecular medicine reports · 2026Review
- Immunosenescence in Older Patients with Psoriasis: Mechanistic Insights and Opportunities for Biologic Therapy.Psoriasis (Auckland, N.Z.) · 2026Review
- Association and Mechanistic Insights Linking Bisphenol A to Psoriasis: Integrative Analysis of Bulk Transcriptomics, Network Toxicology, Mendelian Randomization and Single‑cell RNA Sequencing.Clinical, cosmetic and investigational dermatology · 2026Article
- Integrative Multi-Omics Mendelian Randomization Identifies BLMH as a Keratinocyte-Enriched Protective Candidate Gene and Potential Therapeutic Target in Psoriasis.Journal of inflammation research · 2026Article
- Investigating Overlapping Genetic Factors and Novel Causal Genes in Autoimmune Diseases: A Transcriptome-Wide Association and Multiomics Study.International journal of genomics · 2025Article
- MAIT Cells with High PFN1 Expression Mediate Immune Activation and Metabolic Reprogramming in Psoriasis.Clinical, cosmetic and investigational dermatology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Current pharmacological treatments for psoriasis are generally non-specific and have significant limitations, particularly in the realm of targeted biologic therapies. There is an urgent need to identify and develop new therapeutic targets to improve treatment options. Objective: The aim of this study was to explore the proteome associated with psoriasis in large population cohorts to discover novel biomarkers that could guide therapy. Methods: We analyzed data from 54,306 participants enrolled in the UK Biobank Pharmacological Proteomics Project (UKB-PPP). We investigated the relationship between 2923 serum proteins and the risk of psoriasis using multivariate Cox regression models initially. This was complemented by two-sample Mendelian randomization (TSMR), Summary-data-based Mendelian Randomization (SMR), and coloc colocalization studies to identify genetic correlations with protein targets linked to psoriasis. A protein scoring system was created using the Cox proportional hazards model, and cumulative risk curves were generated to analyze psoriasis incidence variations. Results: Our study pinpointed 62 proteins significantly linked to the risk of developing psoriasis. Further analysis through TSMR narrowed these down to ten proteins with strong causal relationships to the disease. Additional deep-dive analyses such as SMR, colocalization, and differential expression studies highlighted four critical proteins (MMP12, PCSK9, PRSS8, and SCLY). We calculated a protein score based on the levels of these proteins, with higher scores correlating with increased risk of psoriasis. Conclusion: This study's integration of proteomic and genetic data from a European adult cohort provides compelling evidence of several proteins as viable predictive biomarkers and potential therapeutic targets for psoriasis, facilitating the advancement of targeted treatment strategies.
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