A groundbreaking study led by researchers at Newcastle University and Queen Mary University of London has unveiled critical genetic insights that promise to transform the treatment landscape for psoriasis, a complex and chronic inflammatory skin disorder. This new research, published in Communications Medicine, leverages advanced computational methods and artificial intelligence to decode the intricate gene expression patterns across both affected and unaffected skin, as well as blood samples from individuals with psoriasis. By mapping these molecular signatures, scientists have moved a step closer to enabling truly personalized care approaches, addressing the diverse manifestations and severities of this condition.
Psoriasis affects approximately two percent of the UK population and is characterized by persistent skin inflammation, leading to red, scaly plaques that can be intensely itchy and sometimes painful. Beyond the visible skin lesions, psoriasis is associated with systemic inflammation, increasing the risk of several comorbidities such as cardiovascular disease, arthritis, and Type 2 diabetes. Despite its widespread impact and the World Health Organization’s endorsement for personalized therapeutic strategies, clinical progress has been hindered by the absence of dependable biomarkers for guiding treatment.
The researchers undertook a large-scale, integrative analysis encompassing over 700 samples obtained from patients initiating biological therapies. By applying state-of-the-art machine learning algorithms to transcriptomic data—derived from both blood and skin biopsies—the team identified previously unrecognized gene expression patterns correlating with disease severity, metabolic factors such as body mass index (BMI), and specific genetic variants linked to psoriasis risk. This multi-dimensional approach marks one of the most comprehensive examinations to date into the molecular underpinnings of psoriasis.
Among the key findings is the characterization of a 9-gene biomarker panel tightly associated with psoriasis severity. These genes offer a robust molecular signature that could potentially serve as a clinical tool for stratifying patients based on disease activity levels. Additionally, the study highlights two genetic variants, HLADQA101 and HLADRB115, which exhibit strong associations with more severe baseline disease presentations. These insights enhance our understanding of the genetic contributions that predispose individuals to more aggressive forms of psoriasis.
The study further elucidates the role of metabolic factors in psoriasis pathogenesis by identifying a 14-gene expression signature linked to BMI within uninvolved (non-lesional) skin. This signature also correlates with disease severity in lesional skin samples, implying that metabolic dysregulation is a crucial factor influencing disease progression and severity. This connection underscores the complex interplay between genetic predisposition, environmental influences, and systemic health in driving psoriatic pathology.
Intriguingly, blood transcriptomic profiling revealed an immune cell-related gene expression pattern that surfaces exclusively after administration of the biologic drug adalimumab, a TNF-alpha inhibitor commonly used in psoriasis treatment. This finding suggests that specific white blood cell populations are selectively activated or modulated in response to therapy, possibly constituting direct targets of the drug’s anti-inflammatory effects. Understanding these dynamics could guide more effective use of biologic therapies and inform the development of novel immunomodulatory treatments.
Professor Nick Reynolds, senior author and Director of Diagnostics at Newcastle University, emphasized the significance of integrating blood, lesional, and non-lesional skin data. He noted that this comprehensive transcriptomic approach reveals how genetic factors and modifiable environmental aspects such as obesity converge to modulate disease severity and treatment response. These discoveries represent a paradigm shift towards defining distinct psoriasis endotypes that can aid clinical decision-making.
Mike Barnes, co-senior author from Queen Mary University, highlighted the study’s repository as an invaluable resource for the scientific community. The team has made their data accessible through an online portal, allowing researchers worldwide to explore gene signatures and pathways implicated in psoriasis. This open-access framework is expected to accelerate translational research and foster collaborative innovations in dermatology.
The collaborative nature of the PSORT Consortium has been foundational to this breakthrough. With support from funding bodies including the Medical Research Council, the British Association of Dermatologists, and patient organizations such as the Psoriasis Association, the consortium exemplifies how interdisciplinary partnerships can tackle complex biomedical challenges. These alliances have been instrumental in enabling large-scale molecular profiling integrated with clinical data.
Psoriasis remains a lifelong condition with significant variability in onset—typically emerging in two peak age groups during early adulthood and later middle age—and affects men and women equally. Current treatments, especially biologics, have markedly improved outcomes but still face limitations due to heterogeneous patient responses. The molecular biomarkers identified by this study provide a foundation for future stratified medicine approaches, promising not only improved efficacy but also reduced adverse effects.
Beyond advancing clinical care, these findings carry profound implications for patient quality of life. By facilitating early identification of individuals at risk of severe disease and comorbidities, tailored interventions can be implemented to mitigate long-term health complications. This integrative genetics-driven framework supports a move away from one-size-fits-all strategies toward precision dermatology.
Melinda Spencer, Research Manager at the Psoriasis Association, emphasized the hope generated by these insights. She underscored the value of research that can translate directly into more meaningful, personalized treatment options that address the diverse experiences of those living with psoriasis globally.
As the field advances, ongoing research will likely focus on validating these gene signatures in broader populations, exploring mechanistic pathways in greater depth, and integrating multi-omics data layers to capture psoriasis complexity fully. The groundbreaking methodology showcased here sets a precedent for future investigational frameworks across other inflammatory and autoimmune diseases.
This study marks a milestone in dermatological research, illuminating molecular landscapes that underpin psoriasis heterogeneity and treatment response. With continued multidisciplinary collaboration and technological innovation, the vision of personalized, effective treatments that enhance patient outcomes and quality of life is becoming increasingly attainable.
Subject of Research: People
Article Title: Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity
News Publication Date: 21-Jan-2026
Web References:
https://doi.org/10.1038/s43856-025-01325-4
References:
Rider, A., et al. (2026). Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity. Communications Medicine. DOI: 10.1038/s43856-025-01325-4
Image Credits: Newcastle University, UK
Keywords: Diseases and disorders, Human health
Tags: Artificial Intelligence in Medicinebiomarkers for psoriasis treatmentchronic inflammatory disease managementcomputational methods in genomicsgene discovery for psoriasisgenetic insights for skin disordersinflammatory skin disorder researchNewcastle University psoriasis researchpersonalized care approaches for psoriasispersonalized psoriasis therapiespsoriasis comorbidities and riskspsoriasis treatment advancements
