Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.

Détails

Ressource 1Télécharger: BIB_F47427A90637.P001.pdf (1395.52 [Ko])
Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_F47427A90637
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.
Périodique
Journal of Investigative Dermatology
Auteur(s)
Inkeles M.S., Scumpia P.O., Swindell W.R., Lopez D., Teles R.M., Graeber T.G., Meller S., Homey B., Elder J.T., Gilliet M., Modlin R.L., Pellegrini M.
ISSN
1523-1747 (Electronic)
ISSN-L
0022-202X
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
135
Numéro
1
Pages
151-159
Langue
anglais
Notes
Publication types: Journal Article, pdf : Original Article
Résumé
The ability to obtain gene expression profiles from human disease specimens provides an opportunity to identify relevant gene pathways, but is limited by the absence of data sets spanning a broad range of conditions. Here, we analyzed publicly available microarray data from 16 diverse skin conditions in order to gain insight into disease pathogenesis. Unsupervised hierarchical clustering separated samples by disease as well as common cellular and molecular pathways. Disease-specific signatures were leveraged to build a multi-disease classifier, which predicted the diagnosis of publicly and prospectively collected expression profiles with 93% accuracy. In one sample, the molecular classifier differed from the initial clinical diagnosis and correctly predicted the eventual diagnosis as the clinical presentation evolved. Finally, integration of IFN-regulated gene programs with the skin database revealed a significant inverse correlation between IFN-β and IFN-γ programs across all conditions. Our study provides an integrative approach to the study of gene signatures from multiple skin conditions, elucidating mechanisms of disease pathogenesis. In addition, these studies provide a framework for developing tools for personalized medicine toward the precise prediction, prevention, and treatment of disease on an individual level.
Pubmed
Web of science
Open Access
Oui
Création de la notice
02/01/2015 10:28
Dernière modification de la notice
20/08/2019 17:21
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