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

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.

Integrative transcriptome analysis deciphers mechanisms of nickel contact dermatitis.

Background: Nickel-induced allergic contact dermatitis (nACD) remains a major occupational skin disorder, significantly impacting the quality of life of suffering patients. Complex cellular compositional changes and associated immunological pathways are partly resolved in humans; thus, the impact of nACD on human skin needs to be further elucidated.

Methods: To decipher involved immunological players and pathways, human skin biopsies were taken at 0, 2, 48, and 96 hours after nickel patch test in six nickel-allergic patients.