Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.

backgroundTargeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning.

objectiveAlthough various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers.

methodsIn this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients.

resultsWe select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment.

conclusionsOur approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.