Observational studyThe Journal of investigative dermatology2019
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis.
Observational study in The Journal of investigative dermatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 50 citations in OpenAlex.
- Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.Communications medicine · 2026Article
- Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review.Indian dermatology online journal · 2025Article
- Dietary patterns and psoriasis severity in Thai patients: a machine learning approach for small sample data.Scientific reports · 2025Article
- Cross-sectional study of proteomic differences between moderate and severe psoriasis.Scientific reports · 2025Article
- Pharmacogenomics of TNF inhibitors.Frontiers in immunology · 2025Review
- Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review.Current dermatology reports · 2024Article
- Research trends and hotspots in biologics for plaque psoriasis: A bibliometric study from 2004 to 2023.Heliyon · 2024Article
- Skin hepcidin initiates psoriasiform skin inflammation via Fe-driven hyperproliferation and neutrophil recruitment.Nature communications · 2024Article
- Assessment of Treatment-Relevant Immune Biomarkers in Psoriasis and Atopic Dermatitis: Toward Personalized Medicine in Dermatology.The Journal of investigative dermatology · 2023Review
- Disease modification in inflammatory skin disorders: opportunities and challenges.Nature reviews. Drug discovery · 2023Review
- Pharmaco-Omics in Psoriasis: Paving the Way towards Personalized Medicine.International journal of molecular sciences · 2023Review
- Transcriptional Basis of Psoriasis from Large Scale Gene Expression Studies: The Importance of Moving towards a Precision Medicine Approach.International journal of molecular sciences · 2022Review
- Proteomic Studies of Psoriasis.Biomedicines · 2022Review
- IL-17A and TNF-α inhibitors induce multiple molecular changes in psoriasis.Frontiers in immunology · 2022Article
- New Frontiers in Psoriatic Disease Research, Part I: Genetics, Environmental Triggers, Immunology, Pathophysiology, and Precision Medicine.The Journal of investigative dermatology · 2021Review
- Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.Dermatology and therapy · 2020Review
- Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis.International journal of molecular sciences · 2020Review
- Omics-Driven Biomarkers of Psoriasis: Recent Insights, Current Challenges, and Future Prospects.Clinical, cosmetic and investigational dermatology · 2020Review
- Emerging Uses of Artificial Intelligence in Chronic Dermatologic Disease: A Scoping Review.Journal of cutaneous medicine and surgeryArticle
- Artificial intelligence in dermatology and healthcare: An overview.Indian journal of dermatology, venereology and leprologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
Biologic therapies have shown high efficacy in psoriasis, but individual response varies and is poorly understood. To inform biomarker discovery in the Psoriasis Stratification to Optimise Relevant Therapy (i.e., PSORT) study, we evaluated a comprehensive array of omics platforms across three time points and multiple tissues in a pilot investigation of 10 patients with severe psoriasis, treated with the tumor necrosis factor (TNF) inhibitor, etanercept. We used RNA sequencing to analyze mRNA and small RNA transcriptome in blood, lesional and nonlesional skin, and the SOMAscan platform to investigate the serum proteome. Using an integrative systems biology approach, we identified signals of treatment response in genes and pathways associated with TNF signaling, psoriasis pathology, and the major histocompatibility complex region. We found association between clinical response and TNF-regulated genes in blood and skin. Using a combination of differential expression testing, upstream regulator analysis, clustering techniques, and predictive modeling, we show that baseline samples are indicative of patient response to biologic therapies, including signals in blood, which have traditionally been considered unreliable for inference in dermatology. In conclusion, our pilot study provides both an analytical framework and empirical basis to estimate power for larger studies, specifically the ongoing PSORT study, which we show as powered for biomarker discovery and patient stratification.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.