Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis
Authors' conclusion
This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels.
Study context
- Population
- Korean and Caucasian RA cohorts, peripheral blood cell samples before and after initial anti-TNF treatmentKorean and Caucasian cohorts
- Comparison
- Excellent responders vs null responders to anti-TNF biologics
- Outcome
- Response-associated treatment-induced gene expression changes
Methodological detailslongitudinal transcriptomic analysis, two independent cohorts, RNA sequencing, weighted correlation network analysis, key driver analysis
- Longitudinal transcriptomic analysis
- Two independent cohorts
- RNA sequencing
- Weighted correlation network analysis
- Key driver analysis
Main finding
As reported in the abstract
Response-associated transcriptomic changes
Response-associated genes with significantly different treatment-induced expression changes between excellent and null responders
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Response-associated key driver genes in four response-associated networks (type I IFN signalling, myeloid leucocyte activation, B cell activation, NK cell/lymphocyte-mediated cytotoxicity)
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Original publication
Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis. Rheumatology (Oxford). Published 12 August 2023. doi:10.
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