ArticleArthritis research & therapy2022
Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis.
Article in Arthritis research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Mechanistic Study of Yibi Decoction in Treating Rheumatoid ArthritisEndocrine, metabolic & immune disorders drug targets · 2026Article
- multiDEGGs: Single or Multiomic Differential Network Analysis for Biomarker Discovery and Feature Engineering for Predictive Modeling.Computational and structural biotechnology journal · 2026Article
- Pathotype-Specific Expression of Granzyme-Perforin Pathway Genes and Their Association With Clinical Disease Activity in Early Rheumatoid Arthritis and in a Randomized Clinical Trial.Immune network · 2025Article
- Therapeutic gene targets and epigenetic modifications in rheumatoid arthritis: Insights from MTX, JAK inhibitors, and LLDT-8.Biochemistry and biophysics reports · 2025Article
- Glucocorticoid-Mediated Extracellular Matrix Regulation: Implications for Precision Therapy.Biomedicines · 2025Review
- Building a modular and multi-cellular virtual twin of the synovial joint in Rheumatoid Arthritis.NPJ digital medicine · 2024Article
- Predicting treatment response to methotrexate: are we closer to solving the enigma?Rheumatology (Oxford, England) · 2024Article
- Associations Between Rheumatoid Arthritis Clinical Factors and Synovial Cell Types and States.Arthritis & rheumatology (Hoboken, N.J.) · 2024Article
- Large-scale computational modelling of the M1 and M2 synovial macrophages in rheumatoid arthritis.NPJ systems biology and applications · 2024Article
- DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundTo determine whether gene-gene interaction network analysis of RNA sequencing (RNA-Seq) of synovial biopsies in early rheumatoid arthritis (RA) can inform our understanding of RA pathogenesis and yield improved treatment response prediction models.
methodsWe utilized four well curated pathway repositories obtaining 10,537 experimentally evaluated gene-gene interactions. We extracted specific gene-gene interaction networks in synovial RNA-Seq to characterize histologically defined pathotypes in early RA and leverage these synovial specific gene-gene networks to predict response to methotrexate-based disease-modifying anti-rheumatic drug (DMARD) therapy in the Pathobiology of Early Arthritis Cohort (PEAC). Differential interactions identified within each network were statistically evaluated through robust linear regression models. Ability to predict response to DMARD treatment was evaluated by receiver operating characteristic (ROC) curve analysis.
resultsAnalysis comparing different histological pathotypes showed a coherent molecular signature matching the histological changes and highlighting novel pathotype-specific gene interactions and mechanisms. Analysis of responders vs non-responders revealed higher expression of apoptosis regulating gene-gene interactions in patients with good response to conventional synthetic DMARD. Detailed analysis of interactions between pairs of network-linked genes identified the SOCS2/STAT2 ratio as predictive of treatment success, improving ROC area under curve (AUC) from 0.62 to 0.78. We identified a key role for angiogenesis, observing significant statistical interactions between NOS3 (eNOS) and both CAMK1 and eNOS activator AKT3 when comparing responders and non-responders. The ratio of CAMKD2/NOS3 enhanced a prediction model of response improving ROC AUC from 0.63 to 0.73.
conclusionsWe demonstrate a novel, powerful method which harnesses gene interaction networks for leveraging biologically relevant gene-gene interactions leading to improved models for predicting treatment response.
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