Journal Article

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Casement, John, Foulkes, Amy C., Grantham, Henry J., Ewen, Tom, Iqbal, Wasim A., Kalyana-Sundaram, Shanker, Di Meglio, Paola, Henkin, Rafael, Rider, Ashley, Smith, Graham R., Dand, Nick, Griffiths, Christopher E. M., Thomas, Elizabeth, Rajpal, Deepak K., Smith, Kathleen M., Cockell, Simon J., Smith, Catherine H., Stocken, Deborah, Watson, David S., Warren, Richard B., Amarnath, Shoba, Barker, Jonathan N., Barnes, Michael R., Gisby, Jack, Ng, Sandra, Reynolds, Nick J., Zuliani, Paolo, Traini, Christopher

2026

Overview

Background Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. Methods We performed comprehensive RNA sequencing of skin and blood (n = 718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe chronic plaque psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. Results Here we show, using two complementary dimensionality reduction methods, that co-expressed gene modules and factors within skin and blood are significantly associated with psoriasis phenotypes and disease severity. We identify a 14-gene signature negatively associated with BMI in nonlesional skin and with disease severity in lesional skin. Genotype integration reveals that HLA-DQA1*01 and HLA-DRB1*15 genotypes are positively associated with baseline psoriasis severity. Using explainable machine learning models, we define two disease severity-associated gene modules in lesional skin - one positive, one negatively-associated - and a 9-gene signature in lesional skin predictive of disease severity. Disease severity signatures in blood are only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status is independent of disease severity or drug. Conclusions These findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Plain language summary Psoriasis is a common and debilitating skin disease, linked to other inflammatory conditions. A lot is known about what causes psoriasis and the factors that influence it, but doctors still cannot offer personalised treatments. This is because it has been difficult to understand what makes psoriasis more or less severe, why people respond differently to treatment, or why some people develop related diseases. To help address this, we collected skin and blood samples and personal information from people with severe psoriasis across the United Kingdom. Using computer-based methods, we found shared biological processes that link the disease with obesity and help predict its severity. Rider, Grantham, Smith, Watson et al. integrate multiomic data from patients with psoriasis using dimensionality reduction and machine learning techniques. This approach identifies biological relationships between genetic background, clinical features and disease severity, providing insight into disease variability across individuals.

• Publisher: Nature Publishing Group UK,Springer Nature B.V,Nature Publishing Group,Nature Portfolio
• Subject: 38 / 38/39 / 38/91 / 631/61/514/1949 / 692/699/249/1313/1758 / Biopsy / Gene expression / Machine learning / Medicine / Medicine & Public Health / Pathophysiology / Patients / Psoriasis / Skin