The implication of long non-coding RNA expression profile in rheumatoid arthritis: Correlation with treatment response to tumor necrosis factor inhibitor

Abstract

ABSTRACT Objective This study aimed to investigate the linkage of long non-coding RNA (lncRNA) expression profile with etanercept response in rheumatoid arthritis (RA) patients. Methods Peripheral blood mononuclear cell (PBMC) samples were collected from 80 RA patients prior to etanercept treatment. Samples from eight responders and eight non-responders at week 24 (W24) were proposed to RNA-sequencing, then 10 candidate lncRNAs were sorted and their PBMC expressions were validated by reverse transcription quantitative chain reaction (RT-qPCR) in 80 RA patients. Subsequently, clinical response by lncRNA (CRLnc) prediction model was established. Results RNA-sequencing identified 254 up-regulated and 265 down-regulated lncRNAs in W24 responders compared with non-responders, which were enriched in immune or joint related pathways such as B-cell receptor signaling, osteoclast differentiation and T-cell receptor signaling pathways, etc. By reverse transcription quantitative chain reaction (RT-qPCR) validation: Two lncRNAs were correlated with W4 response, three lncRNAs were correlated with W12 response, seven lncRNAs were correlated with W24 response. Subsequently, to construct and validate CRLnc prediction model, 80 RA patients were randomly divided into test set (n = 40) and validation set (n = 40). In the test set, lncRNA RP3-466P17.2 (OR = 9.743, P = .028), RP11-20D14.6 (OR = 10.935, P = .007), RP11-844P9.2 (OR = 0.075, P = .022), and TAS2R64P (OR = 0.044, P = .016) independently related to W24 etanercept response; then CRLnc prediction model integrating these four lncRNAs presented a good value in predicting W24 etanercept response (Area Under Curve (AUC): 0.956, 95%CI: 0.896–1.000). However, in the validation set, the CRLnc prediction model only exhibited a certain value in predicting W24 etanercept response (AUC: 0.753, 95%CI: 0.536–0.969). Conclusions CRLnc prediction model is potentially a useful tool to instruct etanercept treatment in RA patients.