Predicting Genes Involved in Rheumatoid Arthritis
Author Information
Author(s): Xing Heming, McDonagh Paul D., Bienkowska Jadwiga, Cashorali Tanya, Runge Karl, Miller Robert E., DeCaprio Dave, Church Bruce, Roubenoff Ronenn, Khalil Iya G., Carulli John
Primary Institution: Gene Network Sciences, Cambridge, Massachusetts, United States of America
Hypothesis
Can network ensemble simulations of genetic and gene expression data predict genes involved in rheumatoid arthritis?
Conclusion
The study identifies novel therapeutic intervention points for rheumatoid arthritis that may improve drug efficacy.
Supporting Evidence
- 22 transcripts were identified to have new roles in modulating the Disease Activity Score 28 (DAS28).
- 6 transcripts could serve as alternative targets to TNF-α blocker therapies.
- 59 transcripts were predicted to modulate joint counts but not significantly impact DAS28.
Takeaway
Researchers used computer models to find new ways to help people with rheumatoid arthritis, especially those who don't respond to current treatments.
Methodology
The study used Bayesian network models to analyze genetic and gene expression data from rheumatoid arthritis patients before and after treatment.
Potential Biases
Potential bias due to the technical details of data collection and analysis methods.
Limitations
The data was collected from whole blood, which may not directly reflect the disease mechanisms in joint tissues.
Participant Demographics
Patients were recruited from the Autoimmune Biomarkers Collaborative Network, with a focus on those naive to anti-TNF treatment.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website