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Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.


ABSTRACT: Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test reaction biopsies against four contact allergens and two irritants were analyzed via microarray. Coexpression network analysis and Random Forest classification were used to discover potential biomarkers and selected biomarker models were validated in an independent patient group. Differential gene-expression analysis identified major gene-expression changes depending on the stimulus. Random Forest classification identified CD47, BATF, FASLG, RGS16, SYNPO, SELE, PTPN7, WARS, PRC1, EXO1, RRM2, PBK, RAD54L, KIFC1, SPC25, PKMYT, HISTH1A, TPX2, DLGAP5, TPX2, CH25H, and IL37 as potential biomarkers to distinguish allergic and irritant contact dermatitis in human skin. Validation experiments and prediction performances on external testing datasets demonstrated potential applicability of the identified biomarker models in the clinic. Capitalizing on this knowledge, novel diagnostic tools can be developed to guide clinical diagnosis of contact allergies.

SUBMITTER: Fortino V 

PROVIDER: S-EPMC7776829 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Fortino Vittorio V   Wisgrill Lukas L   Werner Paulina P   Suomela Sari S   Linder Nina N   Jalonen Erja E   Suomalainen Alina A   Marwah Veer V   Kero Mia M   Pesonen Maria M   Lundin Johan J   Lauerma Antti A   Aalto-Korte Kristiina K   Greco Dario D   Alenius Harri H   Fyhrquist Nanna N  

Proceedings of the National Academy of Sciences of the United States of America 20201214 52


Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and allergic contact dermatitis remains challenging. Employing integrative transcriptomic analysis and machine-learning approaches, we aimed to decipher disease-related signature genes to find suitable sets of biomarkers. A total of 89 positive patch-test r  ...[more]

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