Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases
Open accessAbstract
Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in “prelesional” skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.
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Analysis of lesional and nonlesional skin samples from CLE, psoriasis, AD, and SSc revealed shared inflammatory profiles (eg, IFN, IL-12 complex) that distinguish from control skin as well as disease-specific features (eg, lack of IL-21 complex signature and increased TGFβ fibroblast signature in SSc).
“Martínez et al (2022) analyzed lesional and nonlesional skin samples from various inflammatory skin diseases (CLE, psoriasis, AD, and SSc), revealing shared inflammatory profiles (eg, IFN, IL-12 complex) that distinguish from control skin as well as distinguishing features specific for each disease (eg, lack of IL-21 complex signature and increased TGFβ fibroblast signature in SSc).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Martínez et al, 2022
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- Authors
- Martínez, B. A., Shrotri, S., Kingsmore, K. M., Bachali, P., Grammer, A. C., & Lipsky, P. E.
- Affiliations
- Ampel BioSolutions (United States) (US)
- Corresponding author
- Martínez, Brittany A.
- Shrotri, Sneha
- Kingsmore, Kathryn M.
- Lipsky, Peter E.
- ORCID
- Venue
- Science Advances
- Published
- 2022-04-29
- Volume
- 8
- Issue
- 17
- Pages
- eabn4776-eabn4776
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC9054015
- OpenAlex
- W4225120914
- Cited by
- 56
- References
- 77
- Citations by year
- 2026: 17 · 2025: 9 · 2024: 18 · 2023: 8 · 2022: 4
- FWCI
- 9.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
- Health Sciences › Medicine › Pathology and Forensic Medicine › Systemic Sclerosis and Related Diseases
- Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
- Keywords
- inflammatory skin diseases, gene expression signatures, lesional skin, non-lesional skin, cutaneous lupus erythematosus, psoriasis, atopic dermatitis, systemic sclerosis, inflammatory gene signature
- MeSH terms
- Machine Learning, Dermatitis, Atopic, Humans, Psoriasis, Skin
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