Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis
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Significance: Contact dermatitis is an inflammatory skin disorder that arises from direct skin contact with irritants or allergens. Representing over 90% of occupational skin disorders, it has a considerable socioeconomic impact, and patients suffering from contact dermatitis can develop a notable physical handicap. Current diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits. However, distinguishing the clinical phenotype of irritant and allergic contact dermatitis, which is important for appropriate therapeutic strategies, remains challenging. This study identifies and validates biomarkers to distinguish allergic and irritant contact dermatitis in human skin, to be used for the development of novel diagnostic methods and to guide clinical diagnosis.
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Phenotyping of allergic contact dermatitis and irritant contact dermatitis with skin biopsy microarray gene expression modules identified biomarker genes such as ADAM8 and CD47 important in ACD and RRM2 important in ICD.
“Fortino et al (2020) phenotyped allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD) with skin biopsy microarray gene expression modules, identifying biomarker genes such as ADAM8 and CD47 important in ACD and RRM2 important in ICD.”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Fortino et al, 2020
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Artificial intelligence-enabled precision medicine for inflammatory skin diseasesPreprint
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- Authors
- Fortino, V., Wisgrill, L., Werner, P., Suomela, S., Linder, N., Jalonen, E., Suomalainen, A., Marwah, V., Kero, M., Pesonen, M., Lundin, J., Lauerma, A., Aalto-Korte, K., Greco, D., Alenius, H., & Fyhrquist, N.
- Affiliations
- University of Eastern Finland (FI)
- Karolinska Institutet (SE)
- Finnish Institute of Occupational Health (FI)
- Uppsala University (SE)
- Helsinki University Hospital (FI)
- University of Helsinki (FI)
- Tampere University (FI)
- Occupational Medicine, Finnish Institute of Occupational Health, 00250 Helsinki, Finland;
- Corresponding author
- Fyhrquist, Nanna
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2020-12-14
- Volume
- 117
- Issue
- 52
- Pages
- 33474-33485
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC7776829
- OpenAlex
- W3112541839
- Cited by
- 99
- References
- 48
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 21 · 2023: 11 · 2022: 25 · 2021: 8 · 2020: 1
- FWCI
- 8.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Uppsala University Publications (Uppsala University) (submittedVersion, other-oa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Tampere University Institutional Repository (Tampere University) (submittedVersion, cc-by-nc-nd)
- Funders
- Työsuojelurahasto (113314)
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd (2018-00601)
- Topics
- Health Sciences › Medicine › Dermatology › Contact Dermatitis and Allergies
- Health Sciences › Medicine › Dermatology › Dermatology and Skin Diseases
- Health Sciences › Medicine › Pharmacology › Drug-Induced Adverse Reactions
- Keywords
- allergic contact dermatitis, irritant contact dermatitis, biomarker discovery, machine learning, skin inflammation, occupational skin disease, diagnostic classification, human skin
- MeSH terms
- Machine Learning, Adult, Algorithms, Allergens, Diagnosis, Differential, Female, Gene Expression Regulation, Humans, Irritants, Leukocytes, Male, Patch Tests, Severity of Illness Index, Skin, Reproducibility of Results, Biomarkers, Dermatitis, Allergic Contact, Dermatitis, Irritant, Databases, Genetic, Gene Regulatory Networks, Transcriptome
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