Franks, Jennifer M;
Toledo, Diana M;
Martyanov, Viktor;
Wang, Yue;
Huang, Suiyuan;
Wood, Tammara A;
Spino, Cathie;
... Whitfield, Michael L; + view all
(2023)
A Genomic Meta-Analysis of Clinical Variables and Their Association with Intrinsic Molecular Subsets in Systemic Sclerosis.
Rheumatology
, 62
(1)
pp. 19-28.
10.1093/rheumatology/keac344.
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Abstract
OBJECTIVES: Four intrinsic molecular subsets (Inflammatory, Fibroproliferative, Limited, Normal-like) have previously been identified in systemic sclerosis (SSc) and are characterized by unique gene expression signatures and pathways. The intrinsic subsets have been linked to improvement with specific therapies. Here, we investigated associations between baseline demographics and intrinsic molecular subsets in a meta-analysis of published datasets. METHODS: Publicly available gene expression data from skin biopsies of 311 SSc patients measured by DNA microarray were classified into the intrinsic molecular subsets. RNA-sequencing data from 84 participants from the ASSET trial were used as a validation cohort. Baseline clinical demographics and intrinsic molecular subsets were tested for statistically significant associations. RESULTS: Males were more likely to be classified in the fibroproliferative subset (p= 0.0046). SSc patients who identified as African American/Black were 2.5x more likely to be classified as fibroproliferative compared with White/Caucasian patients(p= 0.0378). ASSET participants sera positive for anti-RNA pol I and RNA pol III autoantibodies were enriched in the inflammatory subset (p= 5.8E-5, p= 9.3E-5), while anti-Scl-70 was enriched in the fibroproliferative subset. Mean modified Rodnan Skin Score (mRSS) was statistically higher in the inflammatory and fibroproliferative subsets compared with normal-like(p= 0.0027). The average disease duration for inflammatory subset was less than fibroproliferative and normal-like intrinsic subsets (p= 8.8E-4). CONCLUSIONS: We identified multiple statistically significant differences in baseline demographics between the intrinsic subsets which may represent underlying features of disease pathogenesis (e.g. chronological stages of fibrosis) and have implications for treatments that are more likely to work in certain SSc populations.
| Type: | Article |
|---|---|
| Title: | A Genomic Meta-Analysis of Clinical Variables and Their Association with Intrinsic Molecular Subsets in Systemic Sclerosis |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/rheumatology/keac344 |
| Publisher version: | https://doi.org/10.1093/rheumatology/keac344 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Gene Expression, Machine Learning, Systemic sclerosis, clinical associations, meta-analysis |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Inflammation UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10152180 |
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