{"title":"Unveiling Novel Protein Biomarkers for Psoriasis Through Integrated Analysis of Human Plasma Proteomics and Mendelian Randomization.","authors":"Rui Mao, Tongtong Zhang, Ziye Yang, Ji Li","doi":"10.2147/PTT.S492205","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":74589,"journal":{"name":"Psoriasis (Auckland, N.Z.)","volume":"14 ","pages":"179-193"},"PeriodicalIF":5.2000,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11635628/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psoriasis (Auckland, N.Z.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2147/PTT.S492205","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"DERMATOLOGY","Score":null,"Total":0}
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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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