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

被引:45
作者
Fortino, Vittorio [1 ]
Wisgrill, Lukas [2 ,3 ]
Werner, Paulina [3 ]
Suomela, Sari [4 ]
Linder, Nina [5 ,6 ]
Jalonen, Erja [7 ]
Suomalainen, Alina [8 ]
Marwah, Veer [9 ]
Kero, Mia [10 ]
Pesonen, Maria [4 ]
Lundin, Johan [5 ]
Lauerma, Antti [7 ]
Aalto-Korte, Kristiina [4 ]
Greco, Dario [9 ,11 ,12 ]
Alenius, Harri [3 ,8 ]
Fyhrquist, Nanna [3 ,8 ]
机构
[1] Univ Eastern Finland, Inst Biomed, FI-70211 Kuopio, Finland
[2] Med Univ Vienna, Comprehens Ctr Pediat, Dept Pediat & Adolescence Med, Div Neonatol Pediat Intens Care & Neuropediat, A-1090 Vienna, Austria
[3] Karolinska Inst, Inst Environm Med, SE-17177 Stockholm, Sweden
[4] Finnish Inst Occupat Hlth, Occupat Med, Helsinki 00250, Finland
[5] Univ Helsinki, Inst Mol Med, Helsinki 00014, Finland
[6] Uppsala Univ, Dept Womens & Childrens Hlth, Int Maternal & Child Hlth, SE-75185 Uppsala, Sweden
[7] Helsinki Univ Cent Hosp HUCH, Skin & Allergy Hosp, Hus Helsinki 00029, Finland
[8] Univ Helsinki, Dept Bacteriol & Immunol, Med, Helsinki 00014, Finland
[9] Univ Tampere, Fac Med & Life Sci, Tampere 33520, Finland
[10] Helsinki Univ Hosp, HUSLAB, Hus Helsinki 00029, Finland
[11] Univ Tampere, Inst Biomed Technol, Tampere 33520, Finland
[12] Univ Helsinki, Inst Biotechnol, Helsinki 00014, Finland
关键词
allergic contact dermatitis; irritant contact dermatitis; biomarker; machine learning; artificial intelligence; HAND ECZEMA; EXPRESSION; CELLS; CYTOTOXICITY; INFLAMMATION; RESPONSES; SOCIETY; ADAM8;
D O I
10.1073/pnas.2009192117
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:33474 / 33485
页数:12
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