Asset Details
Do you wish to reserve the book?
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease
Do you wish to request the book?
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease
2023
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
