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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 5

Regulator pairs identify subgroups of samples in intrinsic subsets.

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Regulator pairs identify subgroups of samples in intrinsic subsets.
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Shown are the sample distribution of (A) fibroproliferative and (D) inflammatory samples based on the activity scores of given regulator pairs. Group 1 (red dots for fibroproliferative and purple dots for inflammatory) samples have positive scores of both regulators. Group 3 (brown dots) samples have negative scores of both regulators. Group 2 (blue dots) and group 4 (gray dots) samples have 1 positive and 1 negative score. The box plot of MRSS comparisons between groups is shown in B for fibroproliferative and E for inflammatory samples. (C) Box plots for age at diagnosis and disease duration at baseline are shown for each group of fibroproliferative samples. Two-tailed Mann-Whitney-Wilcoxon test P values are listed. (F) Clinical subtypes and stage fractions for each group are shown in inflammatory samples. Fractions of dcSSc and early-stage samples are shown.

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