Circulating microRNAs as biomarkers in patients with allergic rhinitis and asthma Ronaldo P. Panganiban, BS, Yanli Wang, BS, Judie Howrylak, MD, PhD,

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Presentation transcript:

Circulating microRNAs as biomarkers in patients with allergic rhinitis and asthma Ronaldo P. Panganiban, BS, Yanli Wang, BS, Judie Howrylak, MD, PhD, Vernon M. Chinchilli, PhD, Timothy J. Craig, DO, Avery August, PhD, Faoud T. Ishmael, MD, PhD Journal of Allergy and Clinical Immunology Volume 137, Issue 5, Pages 1423-1432 (May 2016) DOI: 10.1016/j.jaci.2016.01.029 Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 1 miRNA expression in healthy, allergic, and asthmatic subjects. A, Heat map showing expression of 39 candidate miRNAs in 19 healthy, 25 allergic, and 35 asthmatic subjects after 2-way unsupervised hierarchical clustering. B, Representative box plots showing differential expression patterns of miRNAs among healthy, allergic, and asthmatic cohorts. miR-937 is shown as a nondifferentially expressed miRNA. Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 2 Predicted inflammatory gene targets of miRNAs. Selected inflammatory genes targeted by each group of miRNAs are depicted in a network diagram. Differentially expressed miRNAs that do not target any of the selected genes are not shown. Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 3 Cluster analysis of miRNA expression in asthmatic patients. A, Heat map showing 2 main clusters of miRNA expression in asthmatic patients. B, Box plot of peripheral eosinophil levels in clusters 1 and 2. Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 4 Results of the random forest model with 6 features and 100 decision trees. A, Plot describing the proportion of each predicted phenotype for subjects of each disease status. B, Multiclass receiver operating characteristic curve analysis with pairwise comparisons of one class versus all other classes. The average area under the curve (AUC) for the 3 receiver operating characteristic curves is 0.9736, indicating that the model performs well in discriminating between positive and negative instances. Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E1 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E2 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E3 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E4 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E5 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E6 Journal of Allergy and Clinical Immunology 2016 137, 1423-1432DOI: (10.1016/j.jaci.2016.01.029) Copyright © 2016 American Academy of Allergy, Asthma & Immunology Terms and Conditions