Circulating Biomarkers for Predicting Infliximab Response in Rheumatoid Arthritis: A Systematic Bioinformatics Analysis

BACKGROUND: Infliximab shows good efficacy in treating refractory rheumatoid arthritis (RA). However, many patients responded poorly and related studies were inconsistent in predictive biomarkers. This study aimed to identify circulating biomarkers for predicting infliximab response in RA. MATERIAL/METHODS: Public databases of Gene Expression Omnibus (GEO) and ArrayExpress were searched for related microarray datasets, focused on the response to infliximab in RA. All peripheral blood samples were collected before infliximab treatment and gene expression profiles were measured using microarray. Differential genes associated with infliximab efficacy were analyzed. The genes recognized by half of the datasets were regarded as candidate biomarkers and validated by prospective datasets. RESULTS: Eight microarray datasets were identified with 374 blood samples of RA patients, among which 191 (51.1%) were diagnosed as non-responders in the subsequent infliximab treatment. Five genes (FKBP1A, FGF12, ANO1, LRRC31, and AKR1D1) were associated with the efficacy and recognized by half of the datasets. The 5-gene model showed a good predictive power in random- and prospective-designed studies, with AUC (area under receiver operating characteristic [ROC] curve)=0.963 and 1.000, and it was also applicable at the early phase of treatment (at week 2) for predicting the response at week 14 (AUC=1.000). In the placebo group, the model failed to predict the response (AUC=0.697), indicating the model’s specificity in infliximab treatment. CONCLUSIONS: The model of FKBP1A, FGF12, ANO1, LRRC31, and AKR1D1 in peripheral blood is useful for efficiently predicting the response to infliximab treatment in rheumatoid arthritis.