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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. Empl...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776829/ https://www.ncbi.nlm.nih.gov/pubmed/33318199 http://dx.doi.org/10.1073/pnas.2009192117 |
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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 |
author_facet | 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 |
author_sort | Fortino, Vittorio |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7776829 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-77768292021-01-12 Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis 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 Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2020-12-29 2020-12-14 /pmc/articles/PMC7776829/ /pubmed/33318199 http://dx.doi.org/10.1073/pnas.2009192117 Text en Copyright © 2020 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/ https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences 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 Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title | Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title_full | Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title_fullStr | Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title_full_unstemmed | Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title_short | Machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
title_sort | machine-learning–driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776829/ https://www.ncbi.nlm.nih.gov/pubmed/33318199 http://dx.doi.org/10.1073/pnas.2009192117 |
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