气候变化领域集成服务门户(CCPortal): Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

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DOI	10.1073/PNAS.2009192117
	Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis
	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.
发表日期	2021
ISSN	00278424
起始页码	33474
结束页码	33485
卷号	117 期号:52
英文摘要	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. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Allergic contact dermatitis; Artificial intelligence; Biomarker; Irritant contact dermatitis; Machine learning
语种	英语
scopus关键词	biological marker; CD47 antigen; contact allergen; allergen; biological marker; irritant agent; transcriptome; adult; Article; BATF gene; CD47 gene; CH25H gene; controlled study; disease association; DLGAP5 gene; EXO1 gene; FASLG gene; female; gene expression; gene function; gene identification; genetic association; HISTH1A gene; human; human tissue; IL37 gene; irritant dermatitis; KIFC1 gene; machine learning; major clinical study; male; microarray analysis; patch test; PBK gene; PKMYT gene; PRC1 gene; prediction; priority journal; PTPN7 gene; RAD54L gene; random forest; RGS16 gene; RRM2 gene; SELE gene; skin allergy; skin biopsy; SPC25 gene; SYNPO gene; TPX2 gene; transcriptomics; validation process; WARS gene; algorithm; differential diagnosis; gene expression regulation; gene regulatory network; genetic database; genetics; irritant dermatitis; leukocyte; metabolism; pathology; reproducibility; severity of illness index; skin; skin allergy; Adult; Algorithms; Allergens; Biomarkers; Databases, Genetic; Dermatitis, Allergic Contact; Dermatitis, Irritant; Diagnosis, Differential; Female; Gene Expression Regulation; Gene Regulatory Networks; Humans; Irritants; Leukocytes; Machine Learning; Male; Patch Tests; Reproducibility of Results; Severity of Illness Index; Skin; Transcriptome
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179647
作者单位	Institute of Biomedicine, University of Eastern Finland, Kuopio, FI-70211, Finland; Division of Neonatology, Pediatric Intensive Care, and Neuropediatrics, Comprehensive Center for Pediatrics, Department of Pediatrics and Adolescence Medicine, Medical University of Vienna, Vienna, 1090, Austria; Institute of Environmental Medicine, Karolinska Institutet, Stockholm, SE-171 77, Sweden; Occupational Medicine, Finnish Institute of Occupational Health, Helsinki, 00250, Finland; Institute for Molecular Medicine, University of Helsinki, Helsinki, 00014, Finland; Department of Women’s and Children’s Health, International Maternal and Child Health, Uppsala University, Uppsala, SE-751 85, Sweden; Skin and Allergy Hospital, Helsinki University Central Hospital (HUCH), Helsinki, 00029 HUS, Finland; Department of Bacteriology and Immunology, Medicum, University of Helsinki, Helsinki, 00014, Finland; Faculty of Medicine and and Life Sciences, University of Tampere, Tampere, 33520, Finland; HUSLAB, Helsinki University H...
推荐引用方式 GB/T 7714	Fortino V.,Wisgrill L.,Werner P.,等. Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis[J],2021,117(52).
APA	Fortino V..,Wisgrill L..,Werner P..,Suomela S..,Linder N..,...&Fyhrquist N..(2021).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,117(52).
MLA	Fortino V.,et al."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 117.52(2021).