Next Article in Journal
Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide
Previous Article in Journal
Major Depressive Disorder: Existing Hypotheses about Pathophysiological Mechanisms and New Genetic Findings
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 13
Issue 4
10.3390/genes13040647
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Construction of a ceRNA Network Related to Rheumatoid Arthritis

by
Zhanya Huang
1,2 and
Nanzhen Kuang
2,*
1
Queen Mary School, Nanchang University, Nanchang 330006, China
2
Department of Immunology, Medical College of Nanchang University, Nanchang 330006, China
*
Author to whom correspondence should be addressed.
Genes 2022, 13(4), 647; https://doi.org/10.3390/genes13040647
Submission received: 9 March 2022 / Revised: 3 April 2022 / Accepted: 5 April 2022 / Published: 6 April 2022
(This article belongs to the Section Molecular Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
(1) Background: Rheumatoid arthritis (RA) is a common systemic autoimmune disease affecting many people and has an unclear and complicated physiological mechanism. The competing endogenous RNA (ceRNA) network plays an essential role in the development and occurrence of various human physiological processes. This study aimed to construct a ceRNA network related to RA. (2) Methods: We explored the GEO database for peripheral blood mononuclear cell (PBMC) samples and then analyzed the RNA of 52 samples (without treatment) to obtain lncRNAs (DELs), miRNAs (DEMs), and mRNAs (DEGs), which can be differentially expressed with statistical significance in the progression of RA. Next, a ceRNA network was constructed, based on the DELs, DEMs, and DEGs. At the same time, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were used to validate the possible function of the ceRNA network. (3) Results: Through our analysis, 389 DELs, 247 DEMs, and 1081 DEGs were screened. After this, a ceRNA network was constructed for further statistical comparisons, including 16 lncRNAs, 1 miRNA, and 15 mRNAs. According to the GO and KEGG analysis, the ceRNA network was mainly enriched in the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway. (4) Conclusions: The novel ceRNA network related to RA that we constructed offers novel insights into and targets for the underlying molecular mechanisms of the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway (both classic and nonclassic pathways) that affect the level of the genetic regulator, which might offer novel ways to treat RA.

1. Introduction

Rheumatoid arthritis (RA) is a systemic autoimmune disease with chronic inflammation of the joints, synovial cell proliferation, and invasive destruction of cartilage and bone, leading to various complications. According to statistics, RA may affect about 1% of the population [1]. Many researchers have shown a variety of signaling pathways [2,3] and candidate genes [4] that are related to RA, but the underlying mechanism is still unclear. Based on this, it is of great significance to analyze the intrinsic mechanisms within RA for offering novel ideas for the treatment of RA.
Currently, many studies have explored the underlying mechanisms within RA. They have indicated that the formation of RA stems from the complex and extensive signal transduction network of various processes, including the disordered function of the autoimmune response, inflammation, and tumor-like cell changes [5]. Moreover, the development of cutting-edge technology and the study of this complex network have enabled a transition from the macroscale, i.e., the macromolecules of biology, to the microscale, i.e., the gene level [6,7]. However, as RA is an autoimmune disease, the related studies are still mostly focused on inflammatory factors [8,9,10], such as interleukin-1 (IL-1), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and granulocyte-macrophage colony stimulating factor (GM-CSF), and pathways to explore the possibility of alleviating RA [11]. Therefore, we thought the construction of a ceRNA network, the network linking the genetic factors and signaling pathways, could be a novel direction.
At the genetic level, the use of noncoding RNAs (ncRNAs) to alleviate RA is a research hotspot [12,13,14]. To be specific, noncoding RNAs (ncRNA) are RNAs that lack the ability to translate into proteins and can be further divided into miRNAs, lncRNAs, and circle RNAs [15], which regulate the expression of mRNA at the level of both transcription and post-transcription [16]. Among them, lncRNAs and miRNAs have been studied more widely. For miRNA, Xu et al. found that exosome-encapsulated miR-6089 interferes with an inflammatory response in RA through targeting the TLR4 included in the signaling pathways of TLRs/NF-κB [17]. Meanwhile, the viability, proliferation, apoptosis, and migration of fibroblast-like synoviocytes (FLS) were found to be regulated by miR-338-5p within RA via targeting NFAT5 [18].
For lncRNA, Zhang et al. documented that the lncRNA HOTAIR can target downstream miR-138 to inhibit the activation of the NF-κB pathway in LPS-treated chondrocytes, which could alleviate the progression of RA, which indicates the importance of lncRNA–miRNA interactions in RA pathogenesis [19]. Furthermore, some evidence has suggested that the function of ncRNA can be more comprehensively discussed within the ceRNA network through an lncRNA–miRNA–mRNA axis within autoimmune diseases [20,21,22,23,24]. According to this principle, Zhang et al. found that the overexpression of the lncRNA ENST00000494760 may sponge up miR-654-5p, promoting the expression of C1QC in RA patients. This novel ceRNA axis can be used as a biomarker [25]. Yang et al. found that CIRCRNA_09505 can act as a miR-6089 sponge to interfere with inflammation through the miR-6089/AKT1/NF-κB axis in CIA mice (an animal model of RA) [26]. Therefore, constructing an RA-related ceRNA network based on the lncRNA–miRNA–mRNA axis has great potential significance for RA research. We assumed that an analysis of the related ceRNA network could provide novel targets for treating RA.
To construct the ceRNA network, we downloaded the microarray data of the lncRNAs, miRNAs, and mRNAs of PBMC samples (GSE101193 and GSE124373). We first screened the DELs, DEMs, and DEGs in these two datasets through GEO2R analysis. We then used the ggalluvial R package to construct lncRNA–miRNA–mRNA triplets with miRcode, miRDB, miRTarBase, and TargetScan based on DELs, DEMs, and DEGs. Finally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were used by the clusterProfiler R package to explore the possible functions of the ceRNA network.
This study discriminated among human RA-related lncRNA, miRNAs, mRNAs, and possible signaling pathways with high statistical significance, which might offer a novel approach to identify pathological mechanisms and potential targets for RA.

2. Materials and Methods

2.1. Data Download

Firstly, we searched the GEO database (Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geoprofiles, accessed on 1 January 2020) for datasets related to rheumatoid arthritis (RA) by using the keywords “rheumatoid arthritis” and “peripheral blood mononuclear cells”. Next, we searched databases that focused on comparing the genetic factors within PBMCs between the RA and control groups that had relatively sufficient samples from humans. Therefore, GSE101193 and GSE124373 were downloaded. For lncRNA expression profiling, 27 PBMC samples from RA patients and 27 PBMC samples from the healthy control were included in the GSE101193 dataset (platform: GPL21827 Agilent-079487 Arraystar Human LncRNA microarray V4). For miRNA expression profiling, 28 PBMC samples from RA patients and 18 PBMC samples from a healthy control were included in the GSE124373 dataset (platform: GPL21572 Affymetrix Multispecies miRNA-4 Array). For gene/mRNA expression profiling, we used the GSE101193 dataset.

2.2. DELs/DEMs/DEGs Screening

This study used GEO2R, a software platform that automatically performs deviation control analysis for differential expression analysis. Firstly, the differentially expressed lncRNAs (DELs, adj.P.Val < 0.05 and |log FC| > 1.5) between RA and normal samples were screened. At the same time, differentially expressed miRNAs (DEMs) between RA and normal samples were screened, with the cutoff criteria of a p-value of < 0.05. In addition, differentially expressed mRNAs (DEGs) between RA and normal samples were screened based on adj.P.Val < 0.05 and |log FC| > 1.5. Next, the DELs, DEMs, and DEGs were used for subsequent analysis.

2.3. CeRNA Network Construction

We used the ggalluvial R package to construct lncRNA–miRNA–mRNA triplets with miRcode (Version 11; http://www.mircode.org/mircode/, accessed on 1 January 2020), miRDB (Version 7.0; http://mirdb.org/, accessed on 1 January 2020), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/index.html, accessed on 1 January 2020) and TargetScan (Version 7.2; http://targetscan.org/vert_72/, accessed on 1 January 2020) from the DELs, DEMs and DEGs.
MiRcode provides miRNA target predictions of the entire human genome, including more than 10,000 lncRNAs. miRDB can provide miRNA targets and functional annotations in the human genome [27,28]. TargetScan can predict miRNA binding sites, and it is very effective in predicting miRNA binding sites in mammals. MiRTarBase specializes in collecting miRNA–mRNA targeting relationships supported by experimental evidence. All databases have sufficient experimental and computational support and are similar in function but different in propensity, so their combined use can improve the quality of research.
Firstly, we predicted the miRNA targeted by the DELs and constructed the lncRNA–miRNA pairs with the miRcode database based on DELs and DEMs. Next, the target genes of these miRNA signatures were acquired using the miRDB, miRTarBase, and TargetScan databases. Genes that existed in all three databases were treated as target genes of these miRNAs. Finally, through a comparison of predicted target genes with essential genes consisting of DEGs, only the remaining overlapped genes and their interaction pairs were used to construct the lncRNA–miRNA–mRNA triplets (the ceRNA network).

2.4. GO and KEGG Enrichment Analysis of the ceRNA Network

In order to explore the possible functions of the ceRNA network, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses were performed by the clusterProfiler R package. For GO analysis, a p-value of < 0.05 indicates statistical significance, and the GO analysis involved three categories, namely molecular function (MF), biological processes (BP), and cellular components (CC). For KEGG analysis, a p-value of < 0.05 was used as the cutoff criterion. The workflow of this study is shown in Figure 1.

3. Results

3.1. DELs/DEMs/DEGs

As we know, lncRNA–miRNA pairs and miRNA–mRNA pairs can form lncRNA–miRNA–mRNA triplets. miRNA can bind to a targeted mRNA to promote mRNA degradation, while an lncRNA can bind to a targeted miRNA to inhibit mRNA degradation. The data were analyzed separately. As shown in Figure 2, 389 DELs (52 upregulated and 337 downregulated) were screened in GSE101193, 247 DEMs (71 upregulated and 176 downregulated) in GSE124373, and 1081 DEGs (97 upregulated and 984 downregulated) were screened in GSE101193. These DELs, DEMs, and DEGs were selected for subsequent analysis.

3.2. The ceRNA Network

As shown in Figure 3A, a ceRNA network, including 16 lncRNAs (especially for hnRNPU, MALAT1, and NEAT1), 1 miRNA (miR-142-3p), and 15 mRNAs (especially for ACSL4, APC, CLOCK, and ROCK), was constructed with p-values smaller than 0.05. Fifteen of the lncRNAs were downregulated and all 15 mRNAs were downregulated in RA (Figure 3B,C).

3.3. GO and KEGG Enrichment Analysis of the ceRNA Network

A GO functional annotation analysis was carried out further to test the underlying biological functions of the ceRNA network. We identified 156 significant GO-BP terms, 14 GO-CC terms, and 38 GO-MF terms (Table 1) with a p-value of < 0.05. The top 15 significant GO terms are shown in Figure 4A. For the GO-BP analysis of the ceRNA network, the Wnt signaling pathway (peptidyl–serine, phosphorylation, peptidyl–serine modification, protein localization to the centrosome, protein localization to the microtubule organizing center) showed significance in RA. In GO-CC analysis, the most enriched terms indicated the significance of the mTOR pathway (TORC2 complex, TOR complex) and the canonical Wnt signaling pathway (β-catenin destruction complex, Wnt signalosome). Meanwhile, the Wnt signaling pathway, especially for nonclassic pathways (Rho GTPase binding), had significance in RA according to the GO-MF terms. In addition, as exhibited in Figure 4B, the KEGG pathway enrichment analysis of the ceRNA network indicated that they were predominately enriched in 10 KEGG pathways (Table 2) based on a p-value of < 0.05. The dopaminergic system (dopaminergic synapse, circadian rhythm) and the Wnt signaling pathway (inositol phosphate metabolism, phosphatidylinositol signaling system, Wnt signaling pathway) were dominant.

4. Discussion

This study constructed an RA-related ceRNA network, screened out the factors related to RA at the gene level as comprehensively as possible, and further inferred the possible pathways from the influence of related genes on RA by GO and KEGG analysis. Mounting experiments have shown that mistakenly expressed ncRNAs, such as lncRNAs and miRNAs, may be dominant contributors to RA’s pathogenesis and progression [19,20,21,22,23,24]. Moreover, according to the ceRNA theory [29], accumulating evidence has also showed that ceRNA networks participate in regulating the viability, proliferation, migration, and apoptosis of fibroblast-like synoviocytes (FLS) within RA [30], providing novel ideas for the clinical treatment of RA progression. For instance, the lncRNA MEG3 can alleviate RA through miR-141 and inactivation of the AKT/mTOR signaling pathway [13]. The lncRNA HOTAIR can alleviate the progression of RA by targeting miR-138 and inhibiting the NF-κB pathway [19]. The lncRNA GAS5 can alleviate RA by regulating the miR-222-3p/Sirt1 signaling axis [31]. Therefore, this study might provide new guidance for the treatment of RA.
However, given that bioinformatics is a relatively new concept in the field of RA, the sample size for gene comparisons is insufficient, which may have resulted in certain false positive or false negative results. On this basis, we found that in our constructed ceRNA network, three lncRNAs (hnRNPU, MALAT1, and NEAT1), one miRNA (miR-142-3p), and four mRNAs (ACSL4, APC, CLOCK, and ROCK) were directly associated with RA [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46]. In addition, six lncRNAs (QKI, EPC1, TNFSF10, DDX3X, RC3H1-IT1, and BRWD1-IT1) and six mRNAs (SMG1, LCOR, IPMK, RICTOR, KIF5B, and HECTD1) were confirmed to play a role in the destruction of cartilage or the promotion of inflammation [47,48,49,50,51,52,53,54,55,56,57,58], which indirectly supports their association with RA. These pieces of evidence are compatible with the findings of this research to a certain extent. Moreover, additional novel genes screened in this article (lncRNAs: ZFR, CLK4, FAM98A, ZEB2, DLEU1, LINC00184, and LINC00342; mRNAs: CEP192, INPP5F, STRN3, EPM2AIP1, and TWF1) might provide new targets for treating RA.
Further, we analyzed the downstream pathways of the ceRNA network by GO and KEGG analysis, and found that the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway may play important roles in RA. On this basis, we also explored the significance of these pathways in existing studies. To be specific, for the mTOR pathway, which has with prominent statistical significance in Figure 4, Kun Chen carried out a study that showed that metformin arrests the G2/M cell cycle of FLS by downregulating the IGF-IR/PI3K/AKT/mTOR pathway, thereby inhibiting the proliferation of FLS and alleviating the progression of RA [59]. In addition, a study has shown that moxibustion can also produce similar effects by inhibiting the mTOR pathway [60]. Moreover, artesunate can alleviate the progression of RA by downregulating the PI3K/AKT/mTOR pathway to inhibit chondrocyte proliferation and accelerate FLS apoptosis and autophagy [61]. This evidence indicates the significance of the mTOR pathway in RA. However, the exciting finding is that the upstream TLR4-MyD88-MAPK signaling and the downstream NF-κB pathway of the mTOR signaling pathway [62] have been regarded as target pathways for treating RA in many studies [63,64]. Most of these articles paid more attention to whether a particular drug could modify the target signaling pathway to decrease the abnormal production of pro-inflammatory cytokines and alleviate RA instead of studying the pathways that might be influenced, such as the mTOR pathway. In this case, the mTOR pathway might play an underlying role in of how MAPK signaling and the NF-κB pathway can slow down the progression of RA. Clinically, Bruyn et al. found that the combination of everolimus (mTOR inhibitor) and trexate (MTX) was better than MTX alone, possibly due to the enhanced inhibition of the mTOR pathway [65,66]. However, treatment with mTOR blockers may have unnecessary pro-inflammatory side effects, such as increased levels of inflammatory markers in RA patients treated with everolimus [65]. Therefore, our screening of targets in this pathway may provide guidance for reducing side effects and clues for precision diagnosis and treatment.
For the dopaminergic system (Figure 4), potential dopamine functions in RA have been widely considered in recent decades [67]. Dopamine can indirectly affect the immune system through prolactin [68,69,70] or can directly affect immune cells through the dopamine receptors (DR) expressed by immune cells [71]. The effects of dopamine are exerted on the basis of the dose-dependent differences and different states (activated and nonactivated) of cells [67], resulting in the different roles of dopamine in the physiologic and pathologic environment. In general, dopamine is believed to inhibit the production of prolactin by stimulating D2-like DR, thus treating RA. Based on a comparison between RA patients and the control group, it was found that the number of D2DR+ B cells in the synovial tissue of RA patients is higher [72] and the number of D3DR+ mast cells is negatively correlated with the progression of the disease [73]. In blood, the number of D2DR+ B cells is positively correlated with the level of TNF in RA, suggesting that D2DR+ B cells are also involved in the systemic inflammatory response [72]. These suggest a link between dopamine and RA, but the experimental results based on this have been inconsistent or even contradictory when it comes to drug therapy. Studies have been conducted on cabergoline, a D2-like agonist, by Mobini et al. [74] and Erb et al. [75]; bromocriptine, a D2-like agonist, by McMurray [76] and Figueroa et al. [77]; and quinagolide, a D2-like agonist, by Eijsbouts et al. [78]. The different results in these experiments are likely due to the universality of the drug’s effects; i.e., the effects of the drug on RA do not necessarily affect the dopamine system alone. Therefore, the current experimental verification cannot accurately explain the specific connection between dopamine and RA. Clinically, abatacept (CTLA-4Ig), a biologic commonly used in RA patients, was found to be dependent on the Wnt pathway by Rosser-Page et al. [79,80]. However, prudent treatment should be exercised in patients with immune insufficiency, otherwise unexpected bone formation may result from a lack of T cells or Wnt-10b [79]. Considering precision medicine at the genetic level has a chance to ameliorate this side effect, target screening based on this pathway has certain significance. In addition, through a clinical trial, Briot et al. found that two commonly used RA treatment drugs anakinra (IL-1 receptor antagonist) and tocilizumab (anti-IL-6 monoclonal antibody) might also depend on the Wnt pathway to function [81]. In this study, we found that two mRNAs, CLOCK and KIF5B, related to RA can regulate the dopaminergic system so that by interfering with these two mRNAs, researchers can more precisely explore the mechanism of action between dopamine and RA, which might provide novel ideas for treating RA.
For the Wnt signaling pathway, RA treatment through the canonical Wnt/β-catenin pathway has been primarily described [82]. Xiao Wang et al. found that capsules of the traditional Chinese medicine compound huangqin qingre chubi may alleviate the progression of RA by inhibiting the CUL4B/Wnt pathway [83]. However, in this study (Figure 4), the protein functions related to noncanonical signaling pathways (protein localization to the microtubule organizing center, rho GTPase binding, the TORC2 complex, the TOR complex, the phosphatidylinositol signaling system) showed more considerable statistical significance than the protein functions related to the canonical signaling pathways (β-catenin destruction complex), which suggests that the noncanonical signaling pathways may be even more critical for RA than canonical signaling pathways, or at least as necessary. This study also indicated the upstream regulators (miRNA and lncRNA) of the Wnt signaling pathway (Figure 3), which might be used as novel targets for treating RA. Among these, the only screened miRNA, miR-142-3p, may be of great research value. It has been shown that upregulation of miR-142-3p alters the effects of the NF-κB pathway and plays a role in the progression of RA [84]. In addition, the NF-κB pathway has been shown to interact with the Wnt pathway to mediate inflammatory responses [85]. Therefore, we suggest that the relationship between these two pathways and miR-142-3p is worthy of further study. Clinically, drugs used to treat RA through the dopamine system mainly focus on cabergoline and bromocriptine [74,75,86,87]. However, clinical evidence in recent years has found that the regulation of the dopamine pathway seems to regulate the progression of RA to a certain extent, but there is no definite treatment mechanism [69]. Therefore, further analysis from the genetic perspective is meaningful.
This study has some limitations because of the lack of experimental verification. Moreover, DEMs and DEGs were screened on the basis of a p-value smaller than 0.05 instead of an adj.P.Value smaller than 0.05 because if adj.P.Val < 0.05 were used as the screening condition, the DEMs and DEGs that can be screened are very few, which would have been insufficient for constructing a ceRNA network. However, this does not necessarily mean that the DEMs and DEGs screened in this study do not have sufficient significance. In fact, when the screening conditions are very strict, the probability of false negatives occurring will also increase. Therefore, we appropriately increased the scope of screening, and discussed the screened miRNA and mRNA in line with previous experiments [59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82] and found that they have a certain physiological significance. Overall, this study illustrated the significant and novel factors from the gene level to the protein level, which may be regarded as experimental targets for treating RA. Furthermore, it described the possible pathways, which may suggest potential experiments on the corresponding genes and proteins. Hence, this research is of great significance for the design of experiments and the better treatment of RA.

5. Conclusions

Our study used public databases to systematically analyze mRNA-miRNA–lncRNA expression profiles related to RA. In total, 16 lncRNAs (especially for hnRNPU, MALAT1, and NEAT1), 1 miRNA (miR-142-3p), and 15 mRNAs (especially for ACSL4, APC, CLOCK, and ROCK) were identified as being involved in the RA PBMC samples, which may imply three RA-related pathways including the mTOR pathway, the dopaminergic system, and the Wnt signaling pathway (both classic pathways and nonclassic pathways). On this basis, the possibility of treating RA based on the ceRNA network and related pathways was discussed. Therefore, our study might provide novel targets for treating RA.

Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Z.H. and N.K. The first draft of the manuscript was written by Z.H. and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

I wish to acknowledge of Nanzhen Kuang, University of Nanchang, for her help in refining the significance of the results of this study.

Conflicts of Interest

All authors declare that there are no conflict of interest.

References

  1. Lee, D.M.; Weinblatt, M.E. Rheumatoid arthritis. Lancet 2001, 358, 903–911. [Google Scholar] [CrossRef]
  2. Benucci, M.; Turchini, S.; Parrochi, P.; Boccaccini, P.; Manetti, R.; Cammelli, E.; Manfredi, M. Correlation between different clinical activity and anti CC-P (anti-cyclic citrullinated peptide antibodies) titres in rheumatoid arthritis treated with three different tumor necrosis factors TNF-α blockers. Recenti. Prog. Med. 2006, 97, 134–139. [Google Scholar] [PubMed]
  3. Lv, Q.; Yin, Y.; Li, X.; Shan, G.; Wu, X.; Liang, D.; Li, Y.; Zhang, X. The status of rheumatoid factor and anti-cyclic citrullinated peptide antibody are not associated with the effect of anti-TNFα agent treatment in patients with rheumatoid arthritis: A meta-analysis. PLoS ONE 2014, 9, e89442. [Google Scholar]
  4. Ekwall, A.K.; Whitaker, J.W.; Hammaker, D.; Bugbee, W.D.; Wang, W.; Firestein, G.S. The Rheumatoid Arthritis Risk Gene LBH Regulates Growth in Fibroblast-like Synoviocytes. Arthritis Rheumatol. 2015, 67, 1193–1202. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Chen, X.; Kuang, N.; Zeng, X.; Zhang, Z.; Li, Y.; Liu, W.; Fu, Y. Effects of daphnetin combined with Bcl2-siRNA on antiapoptotic genes in synovial fibroblasts of rats with collagen-induced arthritis. Mol. Med. Rep. 2018, 17, 884–890. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. He, X.; Zhang, W.; Liao, L.; Fu, X.; Yu, Q.; Jin, Y. Identification and characterization of microRNAs by high through-put sequencing in mesenchymal stem cells and bone tissue from mice of age-related osteoporosis. PLoS ONE 2013, 8, e71895. [Google Scholar] [CrossRef] [PubMed]
  7. Gady, A.L.; Hermans, F.W.; Van de Wal, M.H.; van Loo, E.N.; Visser, R.G.; Bachem, C.W. Implementation of two high through-put techniques in a novel application: Detecting point mutations in large EMS mutated plant populations. Plant. Methods 2009, 5, 13. [Google Scholar] [CrossRef] [Green Version]
  8. Noack, M.; Miossec, P. Selected cytokine pathways in rheumatoid arthritis. Semin. Immunopathol. 2017, 39, 365–383. [Google Scholar] [CrossRef]
  9. Akdis, M.; Aab, A.; Altunbulakli, C.; Azkur, K.; Costa, R.A.; Crameri, R.; Duan, S.; Eiwegger, T.; Eljaszewicz, A.; Ferstl, R.; et al. Interleukins (from IL-1 to IL-38), interferons, transforming growth factor β, and TNF-α: Receptors, functions, and roles in diseases. J. Allergy Clin. Immunol. 2016, 138, 984–1010. [Google Scholar] [CrossRef] [Green Version]
  10. Bartok, B.; Firestein, G.S. Fibroblast-like synoviocytes: Key effector cells in rheumatoid arthritis. Immunol. Rev. 2010, 233, 233–255. [Google Scholar] [CrossRef]
  11. McInnes, I.B.; Schett, G. Pathogenetic insights from the treatment of rheumatoid arthritis. Lancet. 2017, 389, 2328–2337. [Google Scholar] [CrossRef] [Green Version]
  12. Bi, X.; Guo, X.H.; Mo, B.Y.; Wang, M.L.; Luo, X.Q.; Chen, Y.X.; Liu, F.; Olsen, N.; Pan, Y.F.; Zheng, S.G. LncRNA PICSAR promotes cell proliferation, migration and invasion of fibroblast-like synoviocytes by sponging miRNA-4701-5p in rheumatoid arthritis. EBioMedicine 2019, 50, 408–420. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Li, G.; Liu, Y.; Meng, F.; Xia, Z.; Wu, X.; Fang, Y.; Zhang, C.; Zhang, Y.; Liu, D. LncRNA MEG3 inhibits rheumatoid arthritis through miR-141 and inactivation of AKT/mTOR signalling pathway. J. Cell. Mol. Med. 2019, 23, 7116–7120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Wang, J.; Yan, S.; Yang, J.; Lu, H.; Xu, D.; Wang, Z. Non-coding RNAs in Rheumatoid Arthritis: From Bench to Bedside. Front. Immunol. 2019, 10, 3129. [Google Scholar] [CrossRef] [Green Version]
  15. Kapranov, P.; Cawley, S.E.; Drenkow, J.; Bekiranov, S.; Strausberg, R.L.; Fodor, S.P.; Gingeras, T.R. Large-scale transcriptional activity in chromosomes 21 and 22. Science 2002, 296, 916–919. [Google Scholar] [CrossRef] [Green Version]
  16. Esteller, M. Non-coding RNAs in human disease. Nat. Rev. Genet. 2011, 12, 861–874. [Google Scholar] [CrossRef]
  17. Xu, D.; Song, M.; Chai, C.; Wang, J.; Jin, C.; Wang, X.; Cheng, M.; Yan, S. Exosome-encapsulated miR-6089 regulates inflammatory response via targeting TLR4. J. Cell. Physiol. 2019, 234, 1502–1511. [Google Scholar] [CrossRef]
  18. Guo, T.; Ding, H.; Jiang, H.; Bao, N.; Zhou, L.; Zhao, J. miR-338-5p Regulates the Viability, Proliferation, Apoptosis and Migration of Rheumatoid Arthritis Fibroblast-Like Synoviocytes by Targeting NFAT5. Cell. Physiol. Biochem. 2018, 49, 899–910. [Google Scholar] [CrossRef]
  19. Zhang, H.J.; Wei, Q.F.; Wang, S.J.; Zhang, H.J.; Zhang, X.Y.; Geng, Q.; Cui, Y.H.; Wang, X.H. LncRNA HOTAIR alleviates rheumatoid arthritis by targeting miR-138 and inactivating NF-κB pathway. Int. Immunopharmacol. 2017, 50, 283–290. [Google Scholar] [CrossRef]
  20. Chan, J.J.; Tay, Y. Noncoding RNA:RNA Regulatory Networks in Cancer. Int. J. Mol. Sci. 2018, 19, 1310. [Google Scholar] [CrossRef] [Green Version]
  21. Thomson, D.W.; Dinger, M.E. Endogenous microRNA sponges: Evidence and controversy. Nat. Rev. Genet. 2016, 17, 272–283. [Google Scholar] [CrossRef] [PubMed]
  22. Sen, R.; Ghosal, S.; Das, S.; Balti, S.; Chakrabarti, J. Competing endogenous RNA: The key to posttranscriptional regulation. Sci. World J. 2014, 2014, 896206. [Google Scholar] [CrossRef] [PubMed]
  23. Fan, Z.; Gao, S.; Chen, Y.; Xu, B.; Yu, C.; Yue, M.; Tan, X. Integrative analysis of competing endogenous RNA networks reveals the functional lncRNAs in heart failure. J. Cell. Mol. Med. 2018, 22, 4818–4829. [Google Scholar] [CrossRef] [PubMed]
  24. Li, L.J.; Zhao, W.; Tao, S.S.; Leng, R.X.; Fan, Y.G.; Pan, H.F.; Ye, D.Q. Competitive endogenous RNA network: Potential implication for systemic lupus erythematosus. Expert Opin. Ther. Targets 2017, 21, 639–648. [Google Scholar] [CrossRef] [PubMed]
  25. Zhang, Y.; Wang, X.; Li, W.; Wang, H.; Yin, X.; Jiang, F.; Su, X.; Chen, W.; Li, T.; Mao, X.; et al. Inferences of individual differences in response to tripterysium glycosides across patients with Rheumatoid arthritis using a novel ceRNA regulatory axis. Clin. Transl Med. 2020, 10, e185. [Google Scholar] [CrossRef]
  26. Yang, J.; Cheng, M.; Gu, B.; Wang, J.; Yan, S.; Xu, D. CircRNA_09505 aggravates inflammation and joint damage in collagen-induced arthritis mice via miR-6089/AKT1/NF-κB axis. Cell. Death Dis. 2020, 11, 833. [Google Scholar] [CrossRef]
  27. Chen, Y.; Wang, X. miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020, 48, D127–D131. [Google Scholar] [CrossRef] [Green Version]
  28. Liu, W.; Wang, X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 2019, 20, 18. [Google Scholar] [CrossRef]
  29. Salmena, L.; Poliseno, L.; Tay, Y.; Kats, L.; Pandolfi, P.P. A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell 2011, 146, 353–358. [Google Scholar] [CrossRef] [Green Version]
  30. Hong, Z.; Zhang, X.; Zhang, T.; Hu, L.; Liu, R.; Wang, P.; Wang, H.; Yu, Q.; Mei, D.; Xue, Z.; et al. The ROS/GRK2/HIF-1α/NLRP3 Pathway Mediates Pyroptosis of Fibroblast-Like Synoviocytes and the Regulation of Monomer Derivatives of Paeoniflorin. Oxid. Med. Cell. Longev. 2022, 2022, 4566851. [Google Scholar] [CrossRef]
  31. Yang, Z.; Lin, S.D.; Zhan, F.; Liu, Y.; Zhan, Y.W. LncRNA GAS5 alleviates rheumatoid arthritis through regulating miR-222-3p/Sirt1 signalling axis. Autoimmunity 2021, 54, 13–22. [Google Scholar] [CrossRef] [PubMed]
  32. Lu, Y.; Liu, X.; Xie, M.; Liu, M.; Ye, M.; Li, M.; Chen, X.M.; Li, X.; Zhou, R. The NF-κB-Responsive Long Noncoding RNA FIRRE Regulates Posttranscriptional Regulation of Inflammatory Gene Expression through Interacting with hnRNPU. J. Immunol. 2017, 199, 3571–3582. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Yue, J.L.; Zheng, S.F. Analysis of association between MALAT1 haplotype and the severity of normal-tension glaucoma (NTG). J. Cell. Mol. Med. 2021, 25, 9918–9926. [Google Scholar] [CrossRef] [PubMed]
  34. Wan, L.; Liu, J.; Huang, C.; Zhao, L.; Jiang, H.; Liu, L.; Sun, Y.; Xin, L.; Zheng, L. Decreased long-chain non-coding RNA MALAT1 expression and increased hsa-miR155-3p expression involved in Notch signaling pathway regulation in rheumatoid arthritis patients. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 2020, 36, 535–541. [Google Scholar]
  35. Wang, M.; Chen, Y.; Bi, X.; Luo, X.; Hu, Z.; Liu, Y.; Shi, X.; Weng, W.; Mo, B.; Lu, Y.; et al. LncRNA NEAT1_1 suppresses tumor-like biologic behaviors of fibroblast-like synoviocytes by targeting the miR-221-3p/uPAR axis in rheumatoid arthritis. J. Leukoc. Biol. 2022, 111, 641–653. [Google Scholar] [CrossRef]
  36. Guo, T.; Xing, Y.; Chen, Z.; Zhu, H.; Yang, L.; Xiao, Y.; Xu, J. Long Non-Coding RNA NEAT1 Knockdown Alleviates Rheumatoid Arthritis by Reducing IL-18 through p300/CBP Repression. Inflammation 2022, 45, 100–115. [Google Scholar] [CrossRef]
  37. Rao, Y.; Fang, Y.; Tan, W.; Liu, D.; Pang, Y.; Wu, X.; Zhang, C.; Li, G. Delivery of Long Non-coding RNA NEAT1 by Peripheral Blood Monouclear Cells-Derived Exosomes Promotes the Occurrence of Rheumatoid Arthritis via the MicroRNA-23a/MDM2/SIRT6 Axis. Front. Cell. Dev. Biol. 2020, 8, 551681. [Google Scholar] [CrossRef]
  38. Chen, J.; Luo, X.; Liu, M.; Peng, L.; Zhao, Z.; He, C.; He, Y. Silencing long non-coding RNA NEAT1 attenuates rheumatoid arthritis via the MAPK/ERK signalling pathway by downregulating microRNA-129 and microRNA-204. RNA Biol. 2021, 18, 657–668. [Google Scholar] [CrossRef]
  39. Renman, E.; Brink, M.; Ärlestig, L.; Rantapää-Dahlqvist, S.; Lejon, K. Dysregulated microRNA expression in rheumatoid arthritis families-a comparison between rheumatoid arthritis patients, their first-degree relatives, and healthy controls. Clin. Rheumatol. 2021, 40, 2387–2394. [Google Scholar] [CrossRef]
  40. Ling, H.; Li, M.; Yang, C.; Sun, S.; Zhang, W.; Zhao, L.; Xu, N.; Zhang, J.; Shen, Y.; Zhang, X.; et al. Glycine increased ferroptosis via SAM-mediated GPX4 promoter methylation in rheumatoid arthritis. Rheumatology 2022. Online ahead of print. [Google Scholar] [CrossRef]
  41. Bonelli, M.; Scheinecker, C. How does abatacept really work in rheumatoid arthritis? Curr. Opin. Rheumatol. 2018, 30, 295–300. [Google Scholar] [CrossRef] [PubMed]
  42. Miao, C.G.; Yang, Y.Y.; He, X.; Li, X.F.; Huang, C.; Huang, Y.; Zhang, L.; Lv, X.W.; Jin, Y.; Li, J. Wnt signaling pathway in rheumatoid arthritis, with special emphasis on the different roles in synovial inflammation and bone remodeling. Cell Signal. 2013, 25, 2069–2078. [Google Scholar] [CrossRef] [PubMed]
  43. Xiang, K.; Xu, Z.; Hu, Y.Q.; He, Y.S.; Wu, G.C.; Li, T.Y.; Wang, X.R.; Ding, L.H.; Zhang, Q.; Tao, S.S.; et al. Circadian clock genes as promising therapeutic targets for autoimmune diseases. Autoimmun. Rev. 2021, 20, 102866. [Google Scholar] [CrossRef] [PubMed]
  44. Zhu, L.; Chen, T.; Chang, X.; Zhou, R.; Luo, F.; Liu, J.; Zhang, K.; Wang, Y.; Yang, Y.; Long, H.; et al. Salidroside ameliorates arthritis-induced brain cognition deficits by regulating Rho/ROCK/NF-κB pathway. Neuropharmacology 2016, 103, 134–142. [Google Scholar] [CrossRef] [PubMed]
  45. Zanin-Zhorov, A.; Weiss, J.M.; Nyuydzefe, M.S.; Chen, W.; Scher, J.U.; Mo, R.; Depoil, D.; Rao, N.; Liu, B.; Wei, J.; et al. Selective oral ROCK2 inhibitor down-regulates IL-21 and IL-17 secretion in human T cells via STAT3-dependent mechanism. Proc. Natl. Acad. Sci. USA 2014, 111, 16814–16819. [Google Scholar] [CrossRef] [Green Version]
  46. Weng, C.H.; Gupta, S.; Geraghty, P.; Foronjy, R.; Pernis, A.B. Cigarette smoke inhibits ROCK2 activation in T cells and modulates IL-22 production. Mol. Immunol. 2016, 71, 115–122. [Google Scholar] [CrossRef] [Green Version]
  47. Rauwel, B.; Degboé, Y.; Diallo, K.; Sayegh, S.; Baron, M.; Boyer, J.F.; Constantin, A.; Cantagrel, A.; Davignon, J. Inhibition of Osteoclastogenesis by the RNA-Binding Protein QKI5: A Novel Approach to Protect from Bone Resorption. J. Bone Miner. Res. 2020, 35, 753–765. [Google Scholar] [CrossRef]
  48. Wang, T.; Chen, N.; Ren, W.; Liu, F.; Gao, F.; Ye, L.; Han, Y.; Zhang, Y.; Liu, Y. Integrated analysis of circRNAs and mRNAs expression profile revealed the involvement of hsa_circ_0007919 in the pathogenesis of ulcerative colitis. J. Gastroenterol. 2019, 54, 804–818. [Google Scholar] [CrossRef]
  49. Rychkov, D.; Neely, J.; Oskotsky, T.; Yu, S.; Perlmutter, N.; Nititham, J.; Carvidi, A.; Krueger, M.; Gross, A.; Criswell, L.A.; et al. Cross-Tissue Transcriptomic Analysis Leveraging Machine Learning Approaches Identifies New Biomarkers for Rheumatoid Arthritis. Front. Immunol. 2021, 12, 638066. [Google Scholar] [CrossRef]
  50. Liu, D.; Zeng, X.; Li, X.; Cui, C.; Hou, R.; Guo, Z.; Mehta, J.L.; Wang, X. Advances in the molecular mechanisms of NLRP3 inflammasome activators and inactivators. Biochem. Pharmacol. 2020, 175, 113863. [Google Scholar] [CrossRef]
  51. Mino, T.; Murakawa, Y.; Fukao, A.; Vandenbon, A.; Wessels, H.H.; Ori, D.; Uehata, T.; Tartey, S.; Akira, S.; Suzuki, Y.; et al. Regnase-1 and Roquin Regulate a Common Element in Inflammatory mRNAs by Spatiotemporally Distinct Mechanisms. Cell 2015, 161, 1058–1073. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Guo, Z.; Wang, H.; Zhao, F.; Liu, M.; Wang, F.; Kang, M.; He, W.; Lv, Z. Exosomal circ-BRWD1 contributes to osteoarthritis development through the modulation of miR-1277/TRAF6 axis. Arthritis Res. Ther. 2021, 23, 159. [Google Scholar] [CrossRef] [PubMed]
  53. Roberts, T.L.; Ho, U.; Luff, J.; Lee, C.S.; Apte, S.H.; MacDonald, K.P.; Raggat, L.J.; Pettit, A.R.; Morrow, C.A.; Waters, M.J.; et al. Smg1 haploinsufficiency predisposes to tumor formation and inflammation. Proc. Natl. Acad. Sci. USA 2013, 110, E285–E294. [Google Scholar] [CrossRef] [Green Version]
  54. Wang, Y.; Liu, W.; Liu, Y.; Cui, J.; Zhao, Z.; Cao, H.; Fu, Z.; Liu, B. Long noncoding RNA H19 mediates LCoR to impact the osteogenic and adipogenic differentiation of mBMSCs in mice through sponging miR-188. J. Cell. Physiol. 2018, 233, 7435–7446. [Google Scholar] [CrossRef] [PubMed]
  55. Guha, P.; Tyagi, R.; Chowdhury, S.; Reilly, L.; Fu, C.; Xu, R.; Resnick, A.C.; Snyder, S.H. IPMK Mediates Activation of ULK Signaling and Transcriptional Regulation of Autophagy Linked to Liver Inflammation and Regeneration. Cell Rep. 2019, 26, 2692–2703. [Google Scholar] [CrossRef] [Green Version]
  56. Dai, H.; Watson, A.R.; Fantus, D.; Peng, L.; Thomson, A.W.; Rogers, N.M. Rictor deficiency in dendritic cells exacerbates acute kidney injury. Kidney Int. 2018, 94, 951–963. [Google Scholar] [CrossRef]
  57. Yang, S.; Xia, C.; Li, S.; Du, L.; Zhang, L.; Zhou, R. Defective mitophagy driven by dysregulation of rheb and KIF5B contributes to mitochondrial reactive oxygen species (ROS)-induced nod-like receptor 3 (NLRP3) dependent proinflammatory response and aggravates lipotoxicity. Redox Biol. 2014, 3, 63–71. [Google Scholar] [CrossRef] [Green Version]
  58. Zhou, Z.; Jiang, R.; Yang, X.; Guo, H.; Fang, S.; Zhang, Y.; Cheng, Y.; Wang, J.; Yao, H.; Chao, J. circRNA Mediates Silica-Induced Macrophage Activation Via HECTD1/ZC3H12A-Dependent Ubiquitination. Theranostics 2018, 8, 575–592. [Google Scholar] [CrossRef]
  59. Chen, K.; Lin, Z.W.; He, S.M.; Wang, C.Q.; Yang, J.C.; Lu, Y.; Xie, X.B.; Li, Q. Metformin inhibits the proliferation of rheumatoid arthritis fibroblast-like synoviocytes through IGF-IR/PI3K/AKT/m-TOR pathway. Biomed. Pharmacother. 2019, 115, 108875. [Google Scholar] [CrossRef]
  60. Hao, F.; Wang, Q.; Liu, L.; Wu, L.B.; Cai, R.L.; Sang, J.J.; Hu, J.; Wang, J.; Yu, Q.; He, L.; et al. Effect of moxibustion on autophagy and the inflammatory response of synovial cells in rheumatoid arthritis model rat. J. Tradit Chin. Med. 2022, 42, 73–82. [Google Scholar]
  61. Feng, F.B.; Qiu, H.Y. Effects of Artesunate on chondrocyte proliferation, apoptosis and autophagy through the PI3K/AKT/mTOR signaling pathway in rat models with rheumatoid arthritis. Biomed. Pharmacother. 2018, 102, 1209–1220. [Google Scholar] [CrossRef] [PubMed]
  62. Zhou, M.; Xu, W.; Wang, J.; Yan, J.; Shi, Y.; Zhang, C.; Ge, W.; Wu, J.; Du, P.; Chen, Y. Boosting mTOR-dependent autophagy via upstream TLR4-MyD88-MAPK signalling and downstream NF-κB pathway quenches intestinal inflammation and oxidative stress injury. EBioMedicine 2018, 35, 345–360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Mitchell, J.P.; Carmody, R.J. NF-κB and the Transcriptional Control of Inflammation. Int. Rev. Cell Mol. Biol. 2018, 335, 41–84. [Google Scholar] [PubMed]
  64. Liu, F.; Feng, X.X.; Zhu, S.L.; Huang, H.Y.; Chen, Y.D.; Pan, Y.F.; June, R.R.; Zheng, S.G.; Huang, J.L. Sonic Hedgehog Signaling Pathway Mediates Proliferation and Migration of Fibroblast-Like Synoviocytes in Rheumatoid Arthritis via MAPK/ERK Signaling Pathway. Front. Immunol. 2018, 9, 2847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Bruyn, G.A.; Tate, G.; Caeiro, F.; Maldonado-Cocco, J.; Westhovens, R.; Tannenbaum, H.; Bell, M.; Forre, O.; Bjorneboe, O.; Tak, P.P.; et al. Everolimus in patients with rheumatoid arthritis receiving concomitant methotrexate: A 3-month, double-blind, randomised, placebo-controlled, parallel-group, proof-of-concept study. Ann. Rheum. Dis. 2008, 67, 1090–1095. [Google Scholar] [CrossRef] [PubMed]
  66. Suto, T.; Karonitsch, T. The immunobiology of mTOR in autoimmunity. J. Autoimmun. 2020, 110, 102373. [Google Scholar] [CrossRef] [PubMed]
  67. Capellino, S. Dopaminergic Agents in Rheumatoid Arthritis. J. Neuroimmune Pharmacol. 2020, 15, 48–56. [Google Scholar] [CrossRef] [Green Version]
  68. Borba, V.V.; Zandman-Goddard, G.; Shoenfeld, Y. Prolactin and Autoimmunity. Front. Immunol. 2018, 9, 73. [Google Scholar] [CrossRef]
  69. Buckley, A.R. Prolactin, a lymphocyte growth and survival factor. Lupus 2001, 10, 684–690. [Google Scholar] [CrossRef]
  70. Savino, W. Prolactin: An Immunomodulator in Health and Disease. Front. Horm. Res. 2017, 48, 69–75. [Google Scholar]
  71. Arreola, R.; Alvarez-Herrera, S.; Pérez-Sánchez, G.; Becerril-Villanueva, E.; Cruz-Fuentes, C.; Flores-Gutierrez, E.O.; Garcés-Alvarez, M.E.; Cruz-Aguilera, D.L.; Medina-Rivero, E.; Hurtado-Alvarado, G.; et al. Immunomodulatory Effects Mediated by Dopamine. J. Immunol. Res. 2016, 2016, 3160486. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Wei, L.; Sun, Y.; Kong, X.F.; Zhang, C.; Yue, T.; Zhu, Q.; He, D.Y.; Jiang, L.D. The effects of dopamine receptor 2 expression on B cells on bone metabolism and TNF-α levels in rheumatoid arthritis. BMC Musculoskelet. Disord. 2016, 17, 352. [Google Scholar] [CrossRef] [Green Version]
  73. Xue, L.; Li, X.; Chen, Q.; He, J.; Dong, Y.; Wang, J.; Shen, S.; Jia, R.; Zang, Q.J.; Zhang, T.; et al. Associations between D3R expression in synovial mast cells and disease activity and oxidant status in patients with rheumatoid arthritis. Clin. Rheumatol. 2018, 37, 2621–2632. [Google Scholar] [CrossRef] [PubMed]
  74. Mobini, M.; Kashi, Z.; Mohammad Pour, A.R.; Adibi, E. The effect of cabergoline on clinical and laboratory findings in active rheumatoid arthritis. Iran. Red Crescent Med. J. 2011, 13, 749–750. [Google Scholar] [PubMed]
  75. Erb, N.; Pace, A.V.; Delamere, J.P.; Kitas, G.D. Control of unremitting rheumatoid arthritis by the prolactin antagonist cabergoline. Rheumatology 2001, 40, 237–239. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. McMurray, R.W. Bromocriptine in rheumatic and autoimmune diseases. Semin. Arthritis Rheum. 2001, 31, 21–32. [Google Scholar] [CrossRef]
  77. Figueroa, F.E.; Carrión, F.; Martínez, M.E.; Rivero, S.; Mamani, I. Bromocriptine induces immunological changes related to disease parameters in rheumatoid arthritis. Br. J. Rheumatol. 1997, 36, 1022–1023. [Google Scholar] [CrossRef]
  78. Eijsbouts, A.; van den Hoogen, F.; Laan, R.F.; Hermus, R.M.; Sweep, F.C.; van de Putte, L. Treatment of rheumatoid arthritis with the dopamine agonist quinagolide. J. Rheumatol. 1999, 26, 2284–2285. [Google Scholar]
  79. Roser-Page, S.; Vikulina, T.; Zayzafoon, M.; Weitzmann, M.N. CTLA-4Ig-induced T cell anergy promotes Wnt-10b production and bone formation in a mouse model. Arthritis Rheumatol. 2014, 66, 990–999. [Google Scholar] [CrossRef] [Green Version]
  80. Roser-Page, S.; Vikulina, T.; Weiss, D.; Habib, M.M.; Beck, G.R., Jr.; Pacifici, R.; Lane, T.F.; Weitzmann, M.N. CTLA-4Ig (abatacept) balances bone anabolic effects of T cells and Wnt-10b with antianabolic effects of osteoblastic sclerostin. Ann. Acad. Sci. 2018, 1415, 21–33. [Google Scholar] [CrossRef]
  81. Briot, K.; Rouanet, S.; Schaeverbeke, T.; Etchepare, F.; Gaudin, P.; Perdriger, A.; Vray, M.; Steinberg, G.; Roux, C. The effect of tocilizumab on bone mineral density, serum levels of Dickkopf-1 and bone remodeling markers in patients with Rheumatoid Arthritis. Jt. Bone Spine 2015, 82, 109–115. [Google Scholar] [CrossRef] [PubMed]
  82. Movérare-Skrtic, S.; Henning, P.; Liu, X.; Nagano, K.; Saito, H.; E Börjesson, A.; Sjögren, K.; Windahl, S.H.; Farman, H.; Kindlund, B.; et al. Osteoblast-derived WNT16 represses osteoclastogenesis and prevents cortical bone fragility fractures. Nat. Med. 2014, 20, 1279–1288. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Wang, X.; Chang, J.; Zhou, G.; Cheng, C.; Xiong, Y.; Dou, J.; Cheng, G.; Miao, C. The Traditional Chinese Medicine Compound Huangqin Qingre Chubi Capsule Inhibits the Pathogenesis of Rheumatoid Arthritis Through the CUL4B/Wnt Pathway. Front. Pharmacol. 2021, 12, 750233. [Google Scholar] [CrossRef] [PubMed]
  84. Meng, D.; Li, J.; Li, H.; Wang, K. Salvianolic acid B remits LPS-induced injury by up-regulating miR-142-3p in MH7A cells. Biomed. Pharmacother. 2019, 115, 108876. [Google Scholar] [CrossRef] [PubMed]
  85. Nejak-Bowen, K.; Kikuchi, A.; Monga, S.P. β-catenin-NF-κB interactions in murine hepatocytes: A complex to die for. Hepatology 2013, 57, 763–774. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Dougados, M.; Duchesne, L.; Amor, B. Bromocriptine and cyclosporin A combination therapy in rheumatoid arthritis. Arthritis Care Res. 1988, 31, 1333–1334. [Google Scholar] [CrossRef] [PubMed]
  87. Mader, R. Bromocriptine for refractory rheumatoid arthritis. Harefuah 1997, 133, 527–529. [Google Scholar]
Genes 13 00647 g001 550
Figure 1. Workflow of this study.
Figure 1. Workflow of this study.
Genes 13 00647 g001
Genes 13 00647 g002 550
Figure 2. DELs/DEMs/DEGs screening.
Figure 2. DELs/DEMs/DEGs screening.
Genes 13 00647 g002
Genes 13 00647 g003 550
Figure 3. (A) The ceRNA network. (B) Heatmap of lncRNAs in the ceRNA network. (C) Heatmap of mRNAs in the ceRNA network.
Figure 3. (A) The ceRNA network. (B) Heatmap of lncRNAs in the ceRNA network. (C) Heatmap of mRNAs in the ceRNA network.
Genes 13 00647 g003
Genes 13 00647 g004 550
Figure 4. (A) GO and (B) KEGG enrichment analyses of the ceRNA network.
Figure 4. (A) GO and (B) KEGG enrichment analyses of the ceRNA network.
Genes 13 00647 g004
Table
Table 1. GO enrichment analysis of the ceRNA network.
Table 1. GO enrichment analysis of the ceRNA network.
OntologyIDDescriptionBgRatiop-ValueP. AdjustQ-ValueGene IDCount
BPGO:0018105peptidyl–serine phosphorylation310/18,8660.00008460.0276039250.016408959SMG1/ROCK2/RICTOR/INPP5F4
BPGO:0018209peptidyl–serine modification333/18,8660.0001115660.0276039250.016408959SMG1/ROCK2/RICTOR/INPP5F4
BPGO:2000114regulation of the establishment of cell polarity22/18,8660.0001350530.0276039250.016408959ROCK2/RICTOR2
BPGO:1902903regulation of supramolecular fiber organization373/18,8660.0001726870.0276039250.016408959ROCK2/RICTOR/APC/TWF14
BPGO:0032878regulation of the establishment or maintenance of cell polarity25/18,8660.0001751520.0276039250.016408959ROCK2/RICTOR2
BPGO:0071539protein localization to the centrosome31/18,8660.0002707380.0277241760.016480441CEP192/APC2
BPGO:1905508protein localization to the microtubule organizing center33/18,8660.0003071370.0277241760.016480441CEP192/APC2
BPGO:0046486glycerolipid metabolic process434/18,8660.0003082390.0277241760.016480441SMG1/IPMK/INPP5F/ACSL44
BPGO:0033144negative regulation of the intracellular steroid hormone receptor signaling pathway35/18,8660.0003457930.0277241760.016480441CLOCK/STRN32
BPGO:0050796regulation of insulin secretion181/18,8660.000363010.0277241760.016480441CLOCK/ACSL4/KIF5B3
BPGO:0046488phosphatidylinositol metabolic process185/18,8660.0003870130.0277241760.016480441SMG1/IPMK/INPP5F3
BPGO:0030073insulin secretion213/18,8660.0005840740.0354038620.021045576CLOCK/ACSL4/KIF5B3
BPGO:0090276regulation of peptide hormone secretion213/18,8660.0005840740.0354038620.021045576CLOCK/ACSL4/KIF5B3
BPGO:0072698protein localization to the microtubule cytoskeleton53/18,8660.0007942520.0445609490.026488942CEP192/APC2
BPGO:0044380protein localization to the cytoskeleton57/18,8660.0009182150.0445609490.026488942CEP192/APC2
BPGO:0046854phosphatidylinositol phosphorylation57/18,8660.0009182150.0445609490.026488942SMG1/IPMK2
BPGO:0030072peptide hormone secretion257/18,8660.0010070310.0445609490.026488942CLOCK/ACSL4/KIF5B3
BPGO:0003170heart valve development61/18,8660.0010509090.0445609490.026488942ROCK2/HECTD12
BPGO:0046883regulation of hormone secretion267/18,8660.0011243210.0445609490.026488942CLOCK/ACSL4/KIF5B3
BPGO:0030258lipid modification271/18,8660.0011735620.0445609490.026488942SMG1/IPMK/INPP5F3
BPGO:0110053regulation of actin filament organization278/18,8660.0012629890.0445609490.026488942ROCK2/RICTOR/TWF13
BPGO:0051298centrosome duplication68/18,8660.0013039840.0445609490.026488942CEP192/ROCK22
BPGO:1901880negative regulation of protein depolymerization71/18,8660.0014205190.0445609490.026488942APC/TWF12
BPGO:0000281mitotic cytokinesis72/18,8660.0014604340.0445609490.026488942ROCK2/APC2
BPGO:0046834lipid phosphorylation72/18,8660.0014604340.0445609490.026488942SMG1/IPMK2
BPGO:0032024positive regulation of insulin secretion74/18,8660.0015418680.0445609490.026488942ACSL4/KIF5B2
BPGO:0051258protein polymerization300/18,8660.0015718180.0445609490.026488942CEP192/RICTOR/TWF13
BPGO:0033143regulation of the intracellular steroid hormone receptor signaling pathway75/18,8660.0015833840.0445609490.026488942CLOCK/STRN32
BPGO:0070830bicellular tight junction assembly77/18,8660.0016680110.045001110.026750593ROCK2/APC2
BPGO:0120192tight junction assembly79/18,8660.001754760.045001110.026750593ROCK2/APC2
BPGO:0046879hormone secretion314/18,8660.0017910680.045001110.026750593CLOCK/ACSL4/KIF5B3
BPGO:0043242negative regulation of protein-containing complex disassembly81/18,8660.0018436240.045001110.026750593APC/TWF12
BPGO:0120193tight junction organization82/18,8660.0018888480.045001110.026750593ROCK2/APC2
BPGO:0009914hormone transport323/18,8660.0019416720.045001110.026750593CLOCK/ACSL4/KIF5B3
BPGO:0043297apical junction assembly85/18,8660.0020276760.0451819110.026858069ROCK2/APC2
BPGO:0032984protein-containing complex disassembly330/18,8660.0020641480.0451819110.026858069KIF5B/APC/TWF13
BPGO:1901879regulation of protein depolymerization88/18,8660.0021712230.0458271030.027241599APC/TWF12
BPGO:1903829positive regulation of cellular protein localization338/18,8660.0022099360.0458271030.027241599ROCK2/KIF5B/APC3
BPGO:0006650glycerophospholipid metabolic process343/18,8660.0023042460.0465575910.027675832SMG1/IPMK/INPP5F3
BPGO:0050708regulation of protein secretion352/18,8660.002480280.0488615130.029045383CLOCK/ACSL4/KIF5B3
BPGO:0032956regulation of actin cytoskeleton organization360/18,8660.0026436210.0505607790.030055499ROCK2/RICTOR/TWF13
BPGO:0090277positive regulation of peptide hormone secretion99/18,8660.0027376140.0505607790.030055499ACSL4/KIF5B2
BPGO:0061640cytoskeleton-dependent cytokinesis100/18,8660.0027922020.0505607790.030055499ROCK2/APC2
BPGO:0018108peptidyl–tyrosine phosphorylation374/18,8660.002945310.0505607790.030055499RICTOR/INPP5F/TWF13
BPGO:0018212peptidyl–tyrosine modification377/18,8660.0030126190.0505607790.030055499RICTOR/INPP5F/TWF13
BPGO:0003300cardiac muscle hypertrophy104/18,8660.0030156810.0505607790.030055499ROCK2/INPP5F2
BPGO:0010923negative regulation of phosphatase activity104/18,8660.0030156810.0505607790.030055499CEP192/ROCK22
BPGO:0002791regulation of peptide secretion381/18,8660.0031038420.0509547360.030289684CLOCK/ACSL4/KIF5B3
BPGO:0014897striated muscle hypertrophy107/18,8660.003188650.0512787020.030482264ROCK2/INPP5F2
BPGO:0014896muscle hypertrophy109/18,8660.0033065050.0521105120.030976727ROCK2/INPP5F2
BPGO:0035305negative regulation of dephosphorylation111/18,8660.0034263850.052941010.031470411CEP192/ROCK22
BPGO:0051261protein depolymerization115/18,8660.0036722020.0544167890.032347677APC/TWF12
BPGO:0032970regulation of an actin filament-based process405/18,8660.0036872360.0544167890.032347677ROCK2/RICTOR/TWF13
BPGO:0030518intracellular steroid hormone receptor signaling pathway116/18,8660.0037349130.0544167890.032347677CLOCK/STRN32
BPGO:0031109microtubule polymerization or depolymerization117/18,8660.0037981260.0544167890.032347677CEP192/APC2
BPGO:0042752regulation of the circadian rhythm122/18,8660.0041216870.0567595720.033740328ROCK2/CLOCK2
BPGO:0043244regulation of protein-containing complex disassembly122/18,8660.0041216870.0567595720.033740328APC/TWF12
BPGO:0031929TOR signaling124/18,8660.0042545960.0567595720.033740328SMG1/RICTOR2
BPGO:0007098centrosome cycle125/18,8660.0043217950.0567595720.033740328CEP192/ROCK22
BPGO:0043500muscle adaptation125/18,8660.0043217950.0567595720.033740328ROCK2/INPP5F2
BPGO:0007015actin filament organization434/18,8660.0044770020.0578340570.034379048ROCK2/RICTOR/TWF13
BPGO:0046887positive regulation of hormone secretion131/18,8660.0047353540.0601848190.035776442ACSL4/KIF5B2
BPGO:0043434response to peptide hormones447/18,8660.0048620920.0608147350.036150891ROCK2/APC/EPM2AIP13
BPGO:0031023microtubule organizing center organization136/18,8660.0050934850.0619347270.036816663CEP192/ROCK22
BPGO:0006644phospholipid metabolic process455/18,8660.0051088290.0619347270.036816663SMG1/IPMK/INPP5F3
BPGO:0043401steroid hormone-mediated signaling pathway139/18,8660.0053142140.0621487180.036943868CLOCK/STRN32
BPGO:0009306protein secretion462/18,8660.0053308910.0621487180.036943868CLOCK/ACSL4/KIF5B3
BPGO:0035592establishment of protein localization to the extracellular region463/18,8660.0053630870.0621487180.036943868CLOCK/ACSL4/KIF5B3
BPGO:0030010establishment of cell polarity141/18,8660.0054637920.0623980870.037092104ROCK2/RICTOR2
BPGO:0071692protein localization to the extracellular region470/18,8660.0055917870.0629475460.037418726CLOCK/ACSL4/KIF5B3
BPGO:0033135regulation of peptidyl–serine phosphorylation145/18,8660.0057687440.0640249370.038059173RICTOR/INPP5F2
BPGO:0007043cell–cell junction assembly147/18,8660.0059241070.0648360620.03854134ROCK2/APC2
BPGO:0016311dephosphorylation492/18,8660.0063488850.0685331650.040739057CEP192/ROCK2/INPP5F3
BPGO:1902904negative regulation of supramolecular fiber organization156/18,8660.006646880.0707802910.042074846APC/TWF12
BPGO:0051494negative regulation of cytoskeleton organization163/18,8660.0072355450.0708271520.042102702APC/TWF12
BPGO:0042989sequestering of actin monomers10/18,8660.0079243080.0708271520.042102702TWF11
BPGO:0051418microtubule nucleation by the microtubule organizing center10/18,8660.0079243080.0708271520.042102702CEP1921
BPGO:0071394cellular response to testosterone stimulus10/18,8660.0079243080.0708271520.042102702ROCK21
BPGO:0098935dendritic transport10/18,8660.0079243080.0708271520.042102702KIF5B1
BPGO:1902946protein localization to early endosomes10/18,8660.0079243080.0708271520.042102702ROCK21
BPGO:1904779regulation of protein localization to centrosomes10/18,8660.0079243080.0708271520.042102702APC1
BPGO:0030856regulation of epithelial cell differentiation171/18,8660.0079363720.0708271520.042102702ROCK2/CLOCK2
BPGO:0000910cytokinesis172/18,8660.0080260640.0708271520.042102702ROCK2/APC2
BPGO:0050714positive regulation of protein secretion172/18,8660.0080260640.0708271520.042102702ACSL4/KIF5B2
BPGO:0030833regulation of actin filament polymerization174/18,8660.0082068320.0708271520.042102702RICTOR/TWF12
BPGO:0010921regulation of phosphatase activity175/18,8660.0082979070.0708271520.042102702CEP192/ROCK22
BPGO:0007028cytoplasm organization11/18,8660.0087135070.0708271520.042102702KIF5B1
BPGO:0032253dense core granule localization11/18,8660.0087135070.0708271520.042102702KIF5B1
BPGO:0046607positive regulation of the centrosome cycle11/18,8660.0087135070.0708271520.042102702ROCK21
BPGO:0090269fibroblast growth factor production11/18,8660.0087135070.0708271520.042102702ROCK21
BPGO:0090270regulation of fibroblast growth factor production11/18,8660.0087135070.0708271520.042102702ROCK21
BPGO:0099519dense core granule cytoskeletal transport11/18,8660.0087135070.0708271520.042102702KIF5B1
BPGO:1901950dense core granule transport11/18,8660.0087135070.0708271520.042102702KIF5B1
BPGO:1905245regulation of aspartic-type peptidase activity11/18,8660.0087135070.0708271520.042102702ROCK21
BPGO:1905383protein localization to presynapses11/18,8660.0087135070.0708271520.042102702KIF5B1
BPGO:0120032regulation of plasma membrane-bounded cell projection assembly183/18,8660.0090429980.0708271520.042102702APC/TWF12
BPGO:0060491regulation of cell projection assembly185/18,8660.0092338270.0708271520.042102702APC/TWF12
BPGO:0031340positive regulation of vesicle fusion12/18,8660.009502120.0708271520.042102702KIF5B1
BPGO:0032957inositol trisphosphate metabolic process12/18,8660.009502120.0708271520.042102702IPMK1
BPGO:1905668positive regulation of protein localization to the endosome12/18,8660.009502120.0708271520.042102702ROCK21
BPGO:2001135regulation of endocytic recycling12/18,8660.009502120.0708271520.042102702INPP5F1
BPGO:0008064regulation of actin polymerization or depolymerization190/18,8660.0097188130.0708271520.042102702RICTOR/TWF12
BPGO:0070507regulation of microtubule cytoskeleton organization190/18,8660.0097188130.0708271520.042102702ROCK2/APC2
BPGO:0030832regulation of actin filament length191/18,8660.0098171610.0708271520.042102702RICTOR/TWF12
BPGO:0002793positive regulation of peptide secretion193/18,8660.0100152040.0708271520.042102702ACSL4/KIF5B2
BPGO:0030041actin filament polymerization193/18,8660.0100152040.0708271520.042102702RICTOR/TWF12
BPGO:0031274positive regulation of pseudopodium assembly13/18,8660.0102901470.0708271520.042102702APC1
BPGO:0032230positive regulation of synaptic transmission, GABAergic13/18,8660.0102901470.0708271520.042102702KIF5B1
BPGO:0033147negative regulation of the intracellular estrogen receptor signaling pathway13/18,8660.0102901470.0708271520.042102702STRN31
BPGO:0042921glucocorticoid receptor signaling pathway13/18,8660.0102901470.0708271520.042102702CLOCK1
BPGO:0051988regulation of attachment of spindle microtubules to kinetochores13/18,8660.0102901470.0708271520.042102702APC1
BPGO:0099640axo-dendritic protein transport13/18,8660.0102901470.0708271520.042102702KIF5B1
BPGO:1905666regulation of protein localization to endosomes13/18,8660.0102901470.0708271520.042102702ROCK21
BPGO:0009755hormone-mediated signaling pathway200/18,8660.0107224110.0708271520.042102702CLOCK/STRN32
BPGO:0001921positive regulation of receptor recycling14/18,8660.0110775890.0708271520.042102702INPP5F1
BPGO:0031272regulation of pseudopodium assembly14/18,8660.0110775890.0708271520.042102702APC1
BPGO:0031958corticosteroid receptor signaling pathway14/18,8660.0110775890.0708271520.042102702CLOCK1
BPGO:0038166angiotensin-activated signaling pathway14/18,8660.0110775890.0708271520.042102702ROCK21
BPGO:0048681negative regulation of axon regeneration14/18,8660.0110775890.0708271520.042102702INPP5F1
BPGO:0051775response to redox state14/18,8660.0110775890.0708271520.042102702CLOCK1
BPGO:0070672response to interleukin-1514/18,8660.0110775890.0708271520.042102702ACSL41
BPGO:1905205positive regulation of connective tissue replacement14/18,8660.0110775890.0708271520.042102702ROCK21
BPGO:0071383cellular response to steroid hormone stimulus206/18,8660.0113458640.0708271520.042102702CLOCK/STRN32
BPGO:1902905positive regulation of supramolecular fiber organization208/18,8660.0115571990.0708271520.042102702ROCK2/RICTOR2
BPGO:0035303regulation of dephosphorylation209/18,8660.0116635230.0708271520.042102702CEP192/ROCK22
BPGO:0045216cell–cell junction organization210/18,8660.0117702830.0708271520.042102702ROCK2/APC2
BPGO:0032252secretory granule localization15/18,8660.0118644470.0708271520.042102702KIF5B1
BPGO:0070571negative regulation of neuron projection regeneration15/18,8660.0118644470.0708271520.042102702INPP5F1
BPGO:1900037regulation of the cellular response to hypoxia15/18,8660.0118644470.0708271520.042102702ROCK21
BPGO:1901550regulation of endothelial cell development15/18,8660.0118644470.0708271520.042102702ROCK21
BPGO:1903140regulation of establishment of the endothelial barrier15/18,8660.0118644470.0708271520.042102702ROCK21
BPGO:1905203regulation of connective tissue replacement15/18,8660.0118644470.0708271520.042102702ROCK21
BPGO:0007623circadian rhythm218/18,8660.012640010.0725031720.043099ROCK2/CLOCK2
BPGO:0031269pseudopodium assembly16/18,8660.012650720.0725031720.043099APC1
BPGO:0042532negative regulation of tyrosine phosphorylation of STAT protein16/18,8660.012650720.0725031720.043099INPP5F1
BPGO:0045725positive regulation of the glycogen biosynthetic process16/18,8660.012650720.0725031720.043099EPM2AIP11
BPGO:2000651positive regulation of sodium ion transmembrane transporter activity16/18,8660.012650720.0725031720.043099KIF5B1
BPGO:0000075cell cycle checkpoint219/18,8660.0127506710.0725031720.043099CLOCK/APC2
BPGO:0007163establishment or maintenance of cell polarity220/18,8660.0128617620.0725031720.043099ROCK2/RICTOR2
BPGO:0002064epithelial cell development221/18,8660.0129732830.0725031720.043099ROCK2/CLOCK2
BPGO:0008154actin polymerization or depolymerization221/18,8660.0129732830.0725031720.043099RICTOR/TWF12
BPGO:0043624cellular protein complex disassembly224/18,8660.0133104160.0735270150.043707617APC/TWF12
BPGO:0031268pseudopodium organization17/18,8660.0134364090.0735270150.043707617APC1
BPGO:0070875positive regulation of glycogen metabolic process17/18,8660.0134364090.0735270150.043707617EPM2AIP11
BPGO:0051495positive regulation of cytoskeleton organization230/18,8660.0139961880.0760620450.045214547ROCK2/RICTOR2
BPGO:0032271regulation of protein polymerization231/18,8660.0141119680.0761659630.045276321RICTOR/TWF12
BPGO:0035338long-chain fatty acyl–CoA biosynthetic process19/18,8660.0150060370.0804405240.047817303ACSL41
BPGO:0032886regulation of microtubule-based process240/18,8660.0151729070.0807854760.048022358ROCK2/APC2
BPGO:0003323type B pancreatic cell development20/18,8660.0157899760.0807954630.048028294CLOCK1
BPGO:0008090retrograde axonal transport20/18,8660.0157899760.0807954630.048028294KIF5B1
BPGO:0045019negative regulation of a nitric oxide biosynthetic process20/18,8660.0157899760.0807954630.048028294ROCK21
BPGO:0097709connective tissue replacement20/18,8660.0157899760.0807954630.048028294ROCK21
BPGO:1902004positive regulation of amyloid-β formation20/18,8660.0157899760.0807954630.048028294ROCK21
BPGO:1904406negative regulation of a nitric oxide metabolic process20/18,8660.0157899760.0807954630.048028294ROCK21
BPGO:1902307positive regulation of sodium ion transmembrane transport21/18,8660.0165733340.0837165820.049764733KIF5B1
BPGO:1904886β-catenin destruction complex disassembly21/18,8660.0165733340.0837165820.049764733APC1
CCGO:0031932TORC2 complex12/19,5593.13E-050.0017888850.001098438SMG1/RICTOR2
CCGO:0038201TOR complex15/19,5594.97E-050.0017888850.001098438SMG1/RICTOR2
CCGO:0032587ruffle membrane95/19,5590.0020450630.0490815080.030137768APC/TWF12
CCGO:0031256leading edge membrane175/19,5590.0067492080.0702075080.043109873APC/TWF12
CCGO:0001726ruffle179/19,5590.0070506560.0702075080.043109873APC/TWF12
CCGO:0035253ciliary rootlet11/19,5590.0078474940.0702075080.043109873KIF5B1
CCGO:0030877β-catenin destruction complex12/19,5590.0085580590.0702075080.043109873APC1
CCGO:1990909Wnt signalosome12/19,5590.0085580590.0702075080.043109873APC1
CCGO:0033391chromatoid body13/19,5590.0092681520.0702075080.043109873CLOCK1
CCGO:0098554cytoplasmic side of the endoplasmic reticulum membrane15/19,5590.0106869220.0702075080.043109873EPM2AIP11
CCGO:0044233mitochondria-associated endoplasmic reticulum membrane16/19,5590.0113955990.0702075080.043109873ACSL41
CCGO:0036464cytoplasmic ribonucleoprotein granule233/19,5590.0117012510.0702075080.043109873ROCK2/CLOCK2
CCGO:0035770ribonucleoprotein granule243/19,5590.0126776580.0702147210.043114302ROCK2/CLOCK2
CCGO:0000242pericentriolar material21/19,5590.0149319180.0767927220.047153426CEP1921
MFGO:0017048Rho GTPase binding162/18,3520.0075406720.0655169510.030425828ROCK2/STRN32
MFGO:0070016armadillo repeat domain binding10/18,3520.0081454890.0655169510.030425828STRN31
MFGO:0102391decanoate-CoA ligase activity10/18,3520.0081454890.0655169510.030425828ACSL41
MFGO:0031956medium-chain fatty acid–CoA ligase activity11/18,3520.0089566230.0655169510.030425828ACSL41
MFGO:0047676arachidonate-CoA ligase activity11/18,3520.0089566230.0655169510.030425828ACSL41
MFGO:0034595phosphatidylinositol phosphate 5-phosphatase activity12/18,3520.0097671380.0655169510.030425828INPP5F1
MFGO:0035004phosphatidylinositol 3-kinase activity12/18,3520.0097671380.0655169510.030425828IPMK1
MFGO:0045295γ-catenin binding12/18,3520.0097671380.0655169510.030425828APC1
MFGO:0004467long-chain fatty acid–CoA ligase activity13/18,3520.0105770340.0655169510.030425828ACSL41
MFGO:0052745inositol phosphate phosphatase activity13/18,3520.0105770340.0655169510.030425828INPP5F1
MFGO:0019902phosphatase binding194/18,3520.0106631450.0655169510.030425828CEP192/STRN32
MFGO:0003996acyl-CoA ligase activity16/18,3520.0130030140.0655169510.030425828ACSL41
MFGO:0008574ATP-dependent microtubule motor activity, plus-end-directed16/18,3520.0130030140.0655169510.030425828KIF5B1
MFGO:0052744phosphatidylinositol monophosphate phosphatase activity18/18,3520.0146172480.0655169510.030425828INPP5F1
MFGO:0051010microtubule plus-end binding20/18,3520.0162290190.0655169510.030425828APC1
MFGO:0015645fatty acid ligase activity22/18,3520.0178383280.0655169510.030425828ACSL41
MFGO:0017049GTP-rho binding22/18,3520.0178383280.0655169510.030425828ROCK21
MFGO:0050321tau-protein kinase activity22/18,3520.0178383280.0655169510.030425828ROCK21
MFGO:0070840dynein complex binding23/18,3520.0186420610.0655169510.030425828APC1
MFGO:0008017microtubule binding265/18,3520.0192696910.0655169510.030425828KIF5B/APC2
MFGO:0016405CoA-ligase activity26/18,3520.0210495780.0681605380.031653501ACSL41
MFGO:0003785actin monomer binding28/18,3520.0226515260.0700138070.032514152TWF11
MFGO:0016878acid-thiol ligase activity30/18,3520.0242510270.0712325160.033080116ACSL41
MFGO:0051721protein phosphatase 2A binding32/18,3520.0258480840.0712325160.033080116STRN31
MFGO:0052866phosphatidylinositol phosphate phosphatase activity33/18,3520.0266456970.0712325160.033080116INPP5F1
MFGO:1990939ATP-dependent microtubule motor activity34/18,3520.0274427010.0712325160.033080116KIF5B1
MFGO:0042162telomeric DNA binding36/18,3520.0290348820.0712325160.033080116SMG11
MFGO:0045296cadherin binding332/18,3520.0293310360.0712325160.033080116KIF5B/TWF12
MFGO:0016877ligase activity, forming carbon–sulfur bonds40/18,3520.0322119480.0755314650.035076532ACSL41
MFGO:0015631tubulin binding365/18,3520.0349167160.0791445570.036754438KIF5B/APC2
MFGO:0048156tau protein binding45/18,3520.0361696370.0793398480.036845131ROCK21
MFGO:0070888E-box binding50/18,3520.0401122150.0852384580.039584423CLOCK1
MFGO:0004402histone acetyltransferase activity55/18,3520.0440397380.0857036370.039800451CLOCK1
MFGO:0017016Ras GTPase binding415/18,3520.0441074960.0857036370.039800451ROCK2/STRN32
MFGO:0043022ribosome binding57/18,3520.0456065430.0857036370.039800451RICTOR1
MFGO:0061733peptide–lysine–N-acetyltransferase activity57/18,3520.0456065430.0857036370.039800451CLOCK1
MFGO:0031267small GTPase binding428/18,3520.0466328610.0857036370.039800451ROCK2/STRN32
MFGO:0004674protein serine/threonine kinase activity435/18,3520.0480149310.0859214550.039901604SMG1/ROCK22
Table
Table 2. KEGG enrichment analysis of the ceRNA network.
Table 2. KEGG enrichment analysis of the ceRNA network.
IDDescriptionBgRatiop-ValueP. AdjustQ-ValueGene IDCount
hsa00562inositol phosphate metabolism73/81040.0027658870.1280716230.109375964IPMK/INPP5F2
hsa04070phosphatidylinositol signaling system97/81040.0048328910.1280716230.109375964IPMK/INPP5F2
hsa04728dopaminergic synapse132/81040.0087949360.1553771970.132695521CLOCK/KIF5B2
hsa04310Wnt signaling pathway166/81040.0136599290.1809940610.154572882ROCK2/APC2
hsa00061fatty acid biosynthesis18/81040.0198231430.2022276390.172706822ACSL41
hsa04810regulation of the actin cytoskeleton218/81040.0228936950.2022276390.172706822ROCK2/APC2
hsa05132Salmonella infection249/81040.0293536310.2222489170.18980543ROCK2/KIF5B2
hsa04710circadian rhythm31/81040.0339218320.2247321370.191926155CLOCK1
hsa04216ferroptosis41/81040.0446440120.247911260.211721632ACSL41
hsa00071fatty acid degradation43/81040.0467757090.247911260.211721632ACSL41
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Huang, Z.; Kuang, N. Construction of a ceRNA Network Related to Rheumatoid Arthritis. Genes 2022, 13, 647. https://doi.org/10.3390/genes13040647

AMA Style

Huang Z, Kuang N. Construction of a ceRNA Network Related to Rheumatoid Arthritis. Genes. 2022; 13(4):647. https://doi.org/10.3390/genes13040647

Chicago/Turabian Style

Huang, Zhanya, and Nanzhen Kuang. 2022. "Construction of a ceRNA Network Related to Rheumatoid Arthritis" Genes 13, no. 4: 647. https://doi.org/10.3390/genes13040647

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop