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Regulator combinations identify systemic sclerosis patients with more severe disease
Yue Wang, … , Monique Hinchcliff, Michael L. Whitfield
Yue Wang, … , Monique Hinchcliff, Michael L. Whitfield
Published July 28, 2020
Citation Information: JCI Insight. 2020;5(17):e137567. https://doi.org/10.1172/jci.insight.137567.
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Research Article Dermatology

Regulator combinations identify systemic sclerosis patients with more severe disease

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Abstract

Systemic sclerosis (SSc) is a heterogeneous autoimmune disorder that results in skin fibrosis, autoantibody production, and internal organ dysfunction. We previously identified 4 “intrinsic” subsets of SSc based upon skin gene expression that are found across organ systems. Gene expression regulators that underlie the SSc-intrinsic subsets, or are associated with clinical covariates, have not been systematically characterized. Here, we present a computational framework to calculate the activity scores of gene expression regulators and identify their associations with SSc clinical outcomes. We found that regulator activity scores can reproduce the intrinsic molecular subsets, with distinct sets of regulators identified for inflammatory, fibroproliferative, limited, and normal-like samples. Regulators most highly correlated with modified Rodnan skin score (MRSS) also varied by intrinsic subset. We identified subgroups of patients with fibroproliferative and inflammatory SSc with more severe pathophenotypes, such as higher MRSS and increased likelihood of interstitial lung disease (ILD). Using an independent cohort, we show that the group with more severe ILD was more likely to show forced vital capacity decline over a period of 36–54 months. Our results demonstrate an association among the activation of regulators, gene expression subsets, and clinical variables that can identify patients with SSc with more severe disease.

Authors

Yue Wang, Jennifer M. Franks, Monica Yang, Diana M. Toledo, Tammara A. Wood, Monique Hinchcliff, Michael L. Whitfield

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Figure 3

Identification of clinically relevant regulators.

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Identification of clinically relevant regulators.
Heatmaps were plotted ...
Heatmaps were plotted to show the Pearson correlation coefficients (PCC) between activity scores and the MRSS of samples across data sets for (A) fibroproliferative and (B) inflammatory SSc samples. In the heatmap, each row represents a regulator, each column represents a data set, and the cells in the heatmap represents PCC (within which blue is low PCC and red is high PCC). The median PCC was calculated for each regulator across data sets to show the correlation power. The heatmap was plotted by ranking the median PCC in decreasing order. The short, black, bold line is the cutoff we used to select out the common regulators in all data sets. Significant regulators are listed.

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