ArticleScientific reports2023
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Review
- Associations with, and predictors of, progression in systemic sclerosis-related interstitial lung disease: a scoping literature review.European respiratory review : an official journal of the European Respiratory Society · 2026Article
- Multi-omic analysis identifies biological processes underlying progressive interstitial lung disease in systemic sclerosis.The FEBS journal · 2025Article
- Systemic Sclerosis with Interstitial Lung Disease: Identification of Novel Immunogenetic Markers and Ethnic Specificity in Kazakh Patients.Epidemiologia (Basel, Switzerland) · 2025Article
- Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.JCI insight · 2025Article
- An exploratory analysis of differences in serum protein expression by sex in patients with systemic sclerosis associated interstitial lung disease.BMC pulmonary medicine · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc-ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc-ILD using an unbiased, high-throughput approach. We classified SSc-ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc-ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc-ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc-ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc-ILD cohort or application of this study design to a treatment naïve population.
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