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

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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 2020 , ' Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis ' , Proceedings of the National Academy of Sciences of the United States of America , vol. 117 , no. 52 , pp. 33474-33485 . https://doi.org/10.1073/pnas.2009192117

Title: Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis
Author: Fortino, Vittorio; Wisgrill, Lukas; Werner, Paulina; Suomela, Sari; Linder, Nina; Jalonen, Erja; Suomalainen, Alina; Marwah, Veer; Kero, Mia; Pesonen, Maria; Lundin, Johan; Lauerma, Antti; Aalto-Korte, Kristiina; Greco, Dario; Alenius, Harri; Fyhrquist, Nanna
Contributor: University of Helsinki, Medicum
University of Helsinki, Department of Dermatology, Allergology and Venereology
University of Helsinki, Department of Bacteriology and Immunology
University of Helsinki, HUSLAB
University of Helsinki, Finnish Institute of Occupational Health
University of Helsinki, Johan Edvard Lundin / Principal Investigator
University of Helsinki, HUS Inflammation Center
University of Helsinki, Institute of Biotechnology
University of Helsinki, Department of Bacteriology and Immunology
University of Helsinki, Biosciences
Date: 2020-12-29
Language: eng
Number of pages: 12
Belongs to series: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
URI: http://hdl.handle.net/10138/325957
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.
Subject: allergic contact dermatitis
irritant contact dermatitis
biomarker
machine learning
artificial intelligence
HAND ECZEMA
EXPRESSION
CELLS
CYTOTOXICITY
INFLAMMATION
RESPONSES
SOCIETY
ADAM8
3121 General medicine, internal medicine and other clinical medicine
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