Xinfeng capsule is a traditional Chinese medicine compound, which has been clinically used for more than 20 years in the treatment of rheumatoid arthritis (RA), ankylosing spondylitis, osteoarthritis and its extracurricular lesions. However, the molecular role of XFC in the treatment of RA remains unclear. This study aims to explore the efficacy and potential mechanism of XFC through retrospective data mining analysis, animal experiments and cell experiments. The effect of XFC on clinical laboratory indexes of RA patients was observed using data mining techniques combined with association rule analysis and a random walk model. Afterwards, a rat model of adjuvant arthritis (AA) was established with Freund's complete adjuvant, followed by the observation of pathological changes in synovial tissues and the ultrastructure of synoviocytes. A RA cell model was constructed by inducing fibroblast-like synoviocytes (FLSs) with tumor necrosis factor-alpha (TNF-α) to assess the effects of XFC-containing serum on inflammation and oxidative stress through long non-coding RNA LINC00638. In retrospective data mining, XFC effectively reduced immune inflammation and increase the level of antioxidant enzymes in RA patients. Subsequently, animal experiments showed that XFC significantly repressed immune inflammation, oxidative stress, synovial hyperplasia, and cartilage destruction, while improving the ultrastructure of synoviocytes in AA rats. XFC-containing serum diminished the proliferation of TNF-α-induced RA-FLSs, increased LINC00638 expression (P＜0.01), decreased interleukin-6 (IL-6), IL-17, reactive oxygen species (ROS) and reactive nitrogen species (RNS) levels (P＜0.01), and increased the protein expression of nuclear factor erythrocyte 2-related factor 2 (Nrf2), heme oxygenase 1 (HO-1), and superoxide dismutase 2 (SOD2) (P＜0.01). Furthermore, rescue experiments manifested that XFC-containing serum reversed the effects of silencing LINC00638 on inflammation and oxidative stress in RA-FLSs. XFC inhibits inflammation and oxidative stress in RA by up-regulating LINC00638 and activating Nrf2/HO-1 pathway.

Objective: Circular RNAs (circRNAs) are a significant class of molecules involved in a wide range of diverse biological functions that are abnormally expressed in many types of diseases. The present study aimed to determine the circRNAs specifically expressed in peripheral blood mononuclear cells (PBMCs) from rheumatoid arthritis (RA) patients to identify their possible molecular mechanisms. Methods: To identify the circRNAs specifically expressed in RA, we started by sequencing the of PBMCs circRNA and microRNAs (miRNAs) from a RA group (n = 3) and a control group (n = 3). We constructed a network of differentially expressed circRNAs and miRNAs. Then, we selected differentially expressed circRNAs in PBMCs from 10 RA patients relative to 10 age- and sex-matched controls using real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Spearman’s correlation test was used to evaluate the correlation of circRNAs with biochemical measurements. Results: A total of 165 circRNAs and 63 miRNAs were differently expressed between RA patients and healthy people according to RNA-seq, including 109 circRNAs that were significantly up-regulated and 56 circRNAs that were down-regulated among the RA patients. RT-qPCR validation demonstrated that the expression levels of hsa_circ_0001200, hsa_circ_0001566, hsa_circ_0003972, and hsa_circ_0008360 were consistent with the results from the sequencing analysis. Then, we found that there were significant correlations between the circRNAs and disease severity. Conclusion: Generally, these results suggest that expression of hsa_circ_0001200, hsa_circ_0001566, hsa_circ_0003972, and hsa_circ_0008360 in PBMCs from RA patients may serve as potential biomarkers for the diagnosis of RA, and these circRNAs may influence the occurrence and development of RA.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that is diagnosed mainly on the basis of patient signs, symptoms, and laboratory indices. However, the exact causes of RA are unclear. Moreover, there is a lack of any method of dynamically evaluating the efficacy of the medication administered to treat RA. Here, we applied a random walk model to reveal the compatibility among the various constituents of traditional Chinese medicine and evaluate their therapeutic efficacy against RA. Drugs commonly used to treat RA were investigated using cluster analysis. The association rule analysis was applied to identify compatibilities among the constituents. A random walk model was developed to evaluate drug efficacy based on an in‐house database comprising the clinical records of 9,408 RA patients. Frequently administered medicines were combined into three correlated sets. The evaluation based on the random walk method showed that the drug combination improved ESR, CRP, C3, C4, and IgA more effectively than any single drug. The present study demonstrated that the TCM constituents complement each other and various combinations of them produce different therapeutic effects on RA treatment.