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Published on: May 16, 2025
The implication of long non-coding RNA expression profile in rheumatoid arthritis: Correlation with treatment
Qiubo Wang1, Xuan Huang, Yang Shao2
1Department of Clinical Laboratory, Wuxi 9th Affiliated Hospital of Soochow University (Wuxi 9th People's Hospital), Wuxi, China.
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.
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