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.

   Keywords: ceRNA, RA, mTOR pathway, dopaminergic system, Wnt signaling
   pathway

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
   [[26]1]. Many researchers have shown a variety of signaling pathways
   [[27]2,[28]3] and candidate genes [[29]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 [[30]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
   [[31]6,[32]7]. However, as RA is an autoimmune disease, the related
   studies are still mostly focused on inflammatory factors
   [[33]8,[34]9,[35]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 [[36]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 [[37]12,[38]13,[39]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 [[40]15], which regulate the expression of mRNA at the level of
   both transcription and post-transcription [[41]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 [[42]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 [[43]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 [[44]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 [[45]20,[46]21,[47]22,[48]23,[49]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
   [[50]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) [[51]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 ([52]GSE101193 and
   [53]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
   ([54]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, [55]GSE101193 and
   [56]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 [57]GSE101193 dataset (platform: [58]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 [59]GSE124373
   dataset (platform: [60]GPL21572 Affymetrix Multispecies miRNA-4 Array).
   For gene/mRNA expression profiling, we used the [61]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; [62]http://www.mircode.org/mircode/,
   accessed on 1 January 2020), miRDB (Version 7.0; [63]http://mirdb.org/,
   accessed on 1 January 2020), miRTarBase
   ([64]http://mirtarbase.mbc.nctu.edu.tw/index.html, accessed on 1
   January 2020) and TargetScan (Version 7.2;
   [65]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 [[66]27,[67]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 [68]Figure 1.

Figure 1.

   [69]Figure 1
   [70]Open in a new tab

   Workflow of this study.

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 [71]Figure 2, 389 DELs (52 upregulated and 337 downregulated)
   were screened in [72]GSE101193, 247 DEMs (71 upregulated and 176
   downregulated) in [73]GSE124373, and 1081 DEGs (97 upregulated and 984
   downregulated) were screened in [74]GSE101193. These DELs, DEMs, and
   DEGs were selected for subsequent analysis.

Figure 2.

   [75]Figure 2
   [76]Open in a new tab

   DELs/DEMs/DEGs screening.

3.2. The ceRNA Network

   As shown in [77]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 ([78]Figure
   3B,C).

Figure 3.

   [79]Figure 3
   [80]Open in a new tab

   (A) The ceRNA network. (B) Heatmap of lncRNAs in the ceRNA network. (C)
   Heatmap of mRNAs in the ceRNA network.

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 ([81]Table
   1) with a p-value of < 0.05. The top 15 significant GO terms are shown
   in [82]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 [83]Figure
   4B, the KEGG pathway enrichment analysis of the ceRNA network indicated
   that they were predominately enriched in 10 KEGG pathways ([84]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.

Table 1.

   GO enrichment analysis of the ceRNA network.
   Ontology ID Description BgRatio p-Value P. Adjust Q-Value Gene ID Count
   BP GO:0018105 peptidyl–serine phosphorylation 310/18,866 0.0000846
   0.027603925 0.016408959 SMG1/ROCK2/RICTOR/INPP5F 4
   BP GO:0018209 peptidyl–serine modification 333/18,866 0.000111566
   0.027603925 0.016408959 SMG1/ROCK2/RICTOR/INPP5F 4
   BP GO:2000114 regulation of the establishment of cell polarity
   22/18,866 0.000135053 0.027603925 0.016408959 ROCK2/RICTOR 2
   BP GO:1902903 regulation of supramolecular fiber organization
   373/18,866 0.000172687 0.027603925 0.016408959 ROCK2/RICTOR/APC/TWF1 4
   BP GO:0032878 regulation of the establishment or maintenance of cell
   polarity 25/18,866 0.000175152 0.027603925 0.016408959 ROCK2/RICTOR 2
   BP GO:0071539 protein localization to the centrosome 31/18,866
   0.000270738 0.027724176 0.016480441 CEP192/APC 2
   BP GO:1905508 protein localization to the microtubule organizing center
   33/18,866 0.000307137 0.027724176 0.016480441 CEP192/APC 2
   BP GO:0046486 glycerolipid metabolic process 434/18,866 0.000308239
   0.027724176 0.016480441 SMG1/IPMK/INPP5F/ACSL4 4
   BP GO:0033144 negative regulation of the intracellular steroid hormone
   receptor signaling pathway 35/18,866 0.000345793 0.027724176
   0.016480441 CLOCK/STRN3 2
   BP GO:0050796 regulation of insulin secretion 181/18,866 0.00036301
   0.027724176 0.016480441 CLOCK/ACSL4/KIF5B 3
   BP GO:0046488 phosphatidylinositol metabolic process 185/18,866
   0.000387013 0.027724176 0.016480441 SMG1/IPMK/INPP5F 3
   BP GO:0030073 insulin secretion 213/18,866 0.000584074 0.035403862
   0.021045576 CLOCK/ACSL4/KIF5B 3
   BP GO:0090276 regulation of peptide hormone secretion 213/18,866
   0.000584074 0.035403862 0.021045576 CLOCK/ACSL4/KIF5B 3
   BP GO:0072698 protein localization to the microtubule cytoskeleton
   53/18,866 0.000794252 0.044560949 0.026488942 CEP192/APC 2
   BP GO:0044380 protein localization to the cytoskeleton 57/18,866
   0.000918215 0.044560949 0.026488942 CEP192/APC 2
   BP GO:0046854 phosphatidylinositol phosphorylation 57/18,866
   0.000918215 0.044560949 0.026488942 SMG1/IPMK 2
   BP GO:0030072 peptide hormone secretion 257/18,866 0.001007031
   0.044560949 0.026488942 CLOCK/ACSL4/KIF5B 3
   BP GO:0003170 heart valve development 61/18,866 0.001050909 0.044560949
   0.026488942 ROCK2/HECTD1 2
   BP GO:0046883 regulation of hormone secretion 267/18,866 0.001124321
   0.044560949 0.026488942 CLOCK/ACSL4/KIF5B 3
   BP GO:0030258 lipid modification 271/18,866 0.001173562 0.044560949
   0.026488942 SMG1/IPMK/INPP5F 3
   BP GO:0110053 regulation of actin filament organization 278/18,866
   0.001262989 0.044560949 0.026488942 ROCK2/RICTOR/TWF1 3
   BP GO:0051298 centrosome duplication 68/18,866 0.001303984 0.044560949
   0.026488942 CEP192/ROCK2 2
   BP GO:1901880 negative regulation of protein depolymerization 71/18,866
   0.001420519 0.044560949 0.026488942 APC/TWF1 2
   BP GO:0000281 mitotic cytokinesis 72/18,866 0.001460434 0.044560949
   0.026488942 ROCK2/APC 2
   BP GO:0046834 lipid phosphorylation 72/18,866 0.001460434 0.044560949
   0.026488942 SMG1/IPMK 2
   BP GO:0032024 positive regulation of insulin secretion 74/18,866
   0.001541868 0.044560949 0.026488942 ACSL4/KIF5B 2
   BP GO:0051258 protein polymerization 300/18,866 0.001571818 0.044560949
   0.026488942 CEP192/RICTOR/TWF1 3
   BP GO:0033143 regulation of the intracellular steroid hormone receptor
   signaling pathway 75/18,866 0.001583384 0.044560949 0.026488942
   CLOCK/STRN3 2
   BP GO:0070830 bicellular tight junction assembly 77/18,866 0.001668011
   0.04500111 0.026750593 ROCK2/APC 2
   BP GO:0120192 tight junction assembly 79/18,866 0.00175476 0.04500111
   0.026750593 ROCK2/APC 2
   BP GO:0046879 hormone secretion 314/18,866 0.001791068 0.04500111
   0.026750593 CLOCK/ACSL4/KIF5B 3
   BP GO:0043242 negative regulation of protein-containing complex
   disassembly 81/18,866 0.001843624 0.04500111 0.026750593 APC/TWF1 2
   BP GO:0120193 tight junction organization 82/18,866 0.001888848
   0.04500111 0.026750593 ROCK2/APC 2
   BP GO:0009914 hormone transport 323/18,866 0.001941672 0.04500111
   0.026750593 CLOCK/ACSL4/KIF5B 3
   BP GO:0043297 apical junction assembly 85/18,866 0.002027676
   0.045181911 0.026858069 ROCK2/APC 2
   BP GO:0032984 protein-containing complex disassembly 330/18,866
   0.002064148 0.045181911 0.026858069 KIF5B/APC/TWF1 3
   BP GO:1901879 regulation of protein depolymerization 88/18,866
   0.002171223 0.045827103 0.027241599 APC/TWF1 2
   BP GO:1903829 positive regulation of cellular protein localization
   338/18,866 0.002209936 0.045827103 0.027241599 ROCK2/KIF5B/APC 3
   BP GO:0006650 glycerophospholipid metabolic process 343/18,866
   0.002304246 0.046557591 0.027675832 SMG1/IPMK/INPP5F 3
   BP GO:0050708 regulation of protein secretion 352/18,866 0.00248028
   0.048861513 0.029045383 CLOCK/ACSL4/KIF5B 3
   BP GO:0032956 regulation of actin cytoskeleton organization 360/18,866
   0.002643621 0.050560779 0.030055499 ROCK2/RICTOR/TWF1 3
   BP GO:0090277 positive regulation of peptide hormone secretion
   99/18,866 0.002737614 0.050560779 0.030055499 ACSL4/KIF5B 2
   BP GO:0061640 cytoskeleton-dependent cytokinesis 100/18,866 0.002792202
   0.050560779 0.030055499 ROCK2/APC 2
   BP GO:0018108 peptidyl–tyrosine phosphorylation 374/18,866 0.00294531
   0.050560779 0.030055499 RICTOR/INPP5F/TWF1 3
   BP GO:0018212 peptidyl–tyrosine modification 377/18,866 0.003012619
   0.050560779 0.030055499 RICTOR/INPP5F/TWF1 3
   BP GO:0003300 cardiac muscle hypertrophy 104/18,866 0.003015681
   0.050560779 0.030055499 ROCK2/INPP5F 2
   BP GO:0010923 negative regulation of phosphatase activity 104/18,866
   0.003015681 0.050560779 0.030055499 CEP192/ROCK2 2
   BP GO:0002791 regulation of peptide secretion 381/18,866 0.003103842
   0.050954736 0.030289684 CLOCK/ACSL4/KIF5B 3
   BP GO:0014897 striated muscle hypertrophy 107/18,866 0.00318865
   0.051278702 0.030482264 ROCK2/INPP5F 2
   BP GO:0014896 muscle hypertrophy 109/18,866 0.003306505 0.052110512
   0.030976727 ROCK2/INPP5F 2
   BP GO:0035305 negative regulation of dephosphorylation 111/18,866
   0.003426385 0.05294101 0.031470411 CEP192/ROCK2 2
   BP GO:0051261 protein depolymerization 115/18,866 0.003672202
   0.054416789 0.032347677 APC/TWF1 2
   BP GO:0032970 regulation of an actin filament-based process 405/18,866
   0.003687236 0.054416789 0.032347677 ROCK2/RICTOR/TWF1 3
   BP GO:0030518 intracellular steroid hormone receptor signaling pathway
   116/18,866 0.003734913 0.054416789 0.032347677 CLOCK/STRN3 2
   BP GO:0031109 microtubule polymerization or depolymerization 117/18,866
   0.003798126 0.054416789 0.032347677 CEP192/APC 2
   BP GO:0042752 regulation of the circadian rhythm 122/18,866 0.004121687
   0.056759572 0.033740328 ROCK2/CLOCK 2
   BP GO:0043244 regulation of protein-containing complex disassembly
   122/18,866 0.004121687 0.056759572 0.033740328 APC/TWF1 2
   BP GO:0031929 TOR signaling 124/18,866 0.004254596 0.056759572
   0.033740328 SMG1/RICTOR 2
   BP GO:0007098 centrosome cycle 125/18,866 0.004321795 0.056759572
   0.033740328 CEP192/ROCK2 2
   BP GO:0043500 muscle adaptation 125/18,866 0.004321795 0.056759572
   0.033740328 ROCK2/INPP5F 2
   BP GO:0007015 actin filament organization 434/18,866 0.004477002
   0.057834057 0.034379048 ROCK2/RICTOR/TWF1 3
   BP GO:0046887 positive regulation of hormone secretion 131/18,866
   0.004735354 0.060184819 0.035776442 ACSL4/KIF5B 2
   BP GO:0043434 response to peptide hormones 447/18,866 0.004862092
   0.060814735 0.036150891 ROCK2/APC/EPM2AIP1 3
   BP GO:0031023 microtubule organizing center organization 136/18,866
   0.005093485 0.061934727 0.036816663 CEP192/ROCK2 2
   BP GO:0006644 phospholipid metabolic process 455/18,866 0.005108829
   0.061934727 0.036816663 SMG1/IPMK/INPP5F 3
   BP GO:0043401 steroid hormone-mediated signaling pathway 139/18,866
   0.005314214 0.062148718 0.036943868 CLOCK/STRN3 2
   BP GO:0009306 protein secretion 462/18,866 0.005330891 0.062148718
   0.036943868 CLOCK/ACSL4/KIF5B 3
   BP GO:0035592 establishment of protein localization to the
   extracellular region 463/18,866 0.005363087 0.062148718 0.036943868
   CLOCK/ACSL4/KIF5B 3
   BP GO:0030010 establishment of cell polarity 141/18,866 0.005463792
   0.062398087 0.037092104 ROCK2/RICTOR 2
   BP GO:0071692 protein localization to the extracellular region
   470/18,866 0.005591787 0.062947546 0.037418726 CLOCK/ACSL4/KIF5B 3
   BP GO:0033135 regulation of peptidyl–serine phosphorylation 145/18,866
   0.005768744 0.064024937 0.038059173 RICTOR/INPP5F 2
   BP GO:0007043 cell–cell junction assembly 147/18,866 0.005924107
   0.064836062 0.03854134 ROCK2/APC 2
   BP GO:0016311 dephosphorylation 492/18,866 0.006348885 0.068533165
   0.040739057 CEP192/ROCK2/INPP5F 3
   BP GO:1902904 negative regulation of supramolecular fiber organization
   156/18,866 0.00664688 0.070780291 0.042074846 APC/TWF1 2
   BP GO:0051494 negative regulation of cytoskeleton organization
   163/18,866 0.007235545 0.070827152 0.042102702 APC/TWF1 2
   BP GO:0042989 sequestering of actin monomers 10/18,866 0.007924308
   0.070827152 0.042102702 TWF1 1
   BP GO:0051418 microtubule nucleation by the microtubule organizing
   center 10/18,866 0.007924308 0.070827152 0.042102702 CEP192 1
   BP GO:0071394 cellular response to testosterone stimulus 10/18,866
   0.007924308 0.070827152 0.042102702 ROCK2 1
   BP GO:0098935 dendritic transport 10/18,866 0.007924308 0.070827152
   0.042102702 KIF5B 1
   BP GO:1902946 protein localization to early endosomes 10/18,866
   0.007924308 0.070827152 0.042102702 ROCK2 1
   BP GO:1904779 regulation of protein localization to centrosomes
   10/18,866 0.007924308 0.070827152 0.042102702 APC 1
   BP GO:0030856 regulation of epithelial cell differentiation 171/18,866
   0.007936372 0.070827152 0.042102702 ROCK2/CLOCK 2
   BP GO:0000910 cytokinesis 172/18,866 0.008026064 0.070827152
   0.042102702 ROCK2/APC 2
   BP GO:0050714 positive regulation of protein secretion 172/18,866
   0.008026064 0.070827152 0.042102702 ACSL4/KIF5B 2
   BP GO:0030833 regulation of actin filament polymerization 174/18,866
   0.008206832 0.070827152 0.042102702 RICTOR/TWF1 2
   BP GO:0010921 regulation of phosphatase activity 175/18,866 0.008297907
   0.070827152 0.042102702 CEP192/ROCK2 2
   BP GO:0007028 cytoplasm organization 11/18,866 0.008713507 0.070827152
   0.042102702 KIF5B 1
   BP GO:0032253 dense core granule localization 11/18,866 0.008713507
   0.070827152 0.042102702 KIF5B 1
   BP GO:0046607 positive regulation of the centrosome cycle 11/18,866
   0.008713507 0.070827152 0.042102702 ROCK2 1
   BP GO:0090269 fibroblast growth factor production 11/18,866 0.008713507
   0.070827152 0.042102702 ROCK2 1
   BP GO:0090270 regulation of fibroblast growth factor production
   11/18,866 0.008713507 0.070827152 0.042102702 ROCK2 1
   BP GO:0099519 dense core granule cytoskeletal transport 11/18,866
   0.008713507 0.070827152 0.042102702 KIF5B 1
   BP GO:1901950 dense core granule transport 11/18,866 0.008713507
   0.070827152 0.042102702 KIF5B 1
   BP GO:1905245 regulation of aspartic-type peptidase activity 11/18,866
   0.008713507 0.070827152 0.042102702 ROCK2 1
   BP GO:1905383 protein localization to presynapses 11/18,866 0.008713507
   0.070827152 0.042102702 KIF5B 1
   BP GO:0120032 regulation of plasma membrane-bounded cell projection
   assembly 183/18,866 0.009042998 0.070827152 0.042102702 APC/TWF1 2
   BP GO:0060491 regulation of cell projection assembly 185/18,866
   0.009233827 0.070827152 0.042102702 APC/TWF1 2
   BP GO:0031340 positive regulation of vesicle fusion 12/18,866
   0.00950212 0.070827152 0.042102702 KIF5B 1
   BP GO:0032957 inositol trisphosphate metabolic process 12/18,866
   0.00950212 0.070827152 0.042102702 IPMK 1
   BP GO:1905668 positive regulation of protein localization to the
   endosome 12/18,866 0.00950212 0.070827152 0.042102702 ROCK2 1
   BP GO:2001135 regulation of endocytic recycling 12/18,866 0.00950212
   0.070827152 0.042102702 INPP5F 1
   BP GO:0008064 regulation of actin polymerization or depolymerization
   190/18,866 0.009718813 0.070827152 0.042102702 RICTOR/TWF1 2
   BP GO:0070507 regulation of microtubule cytoskeleton organization
   190/18,866 0.009718813 0.070827152 0.042102702 ROCK2/APC 2
   BP GO:0030832 regulation of actin filament length 191/18,866
   0.009817161 0.070827152 0.042102702 RICTOR/TWF1 2
   BP GO:0002793 positive regulation of peptide secretion 193/18,866
   0.010015204 0.070827152 0.042102702 ACSL4/KIF5B 2
   BP GO:0030041 actin filament polymerization 193/18,866 0.010015204
   0.070827152 0.042102702 RICTOR/TWF1 2
   BP GO:0031274 positive regulation of pseudopodium assembly 13/18,866
   0.010290147 0.070827152 0.042102702 APC 1
   BP GO:0032230 positive regulation of synaptic transmission, GABAergic
   13/18,866 0.010290147 0.070827152 0.042102702 KIF5B 1
   BP GO:0033147 negative regulation of the intracellular estrogen
   receptor signaling pathway 13/18,866 0.010290147 0.070827152
   0.042102702 STRN3 1
   BP GO:0042921 glucocorticoid receptor signaling pathway 13/18,866
   0.010290147 0.070827152 0.042102702 CLOCK 1
   BP GO:0051988 regulation of attachment of spindle microtubules to
   kinetochores 13/18,866 0.010290147 0.070827152 0.042102702 APC 1
   BP GO:0099640 axo-dendritic protein transport 13/18,866 0.010290147
   0.070827152 0.042102702 KIF5B 1
   BP GO:1905666 regulation of protein localization to endosomes 13/18,866
   0.010290147 0.070827152 0.042102702 ROCK2 1
   BP GO:0009755 hormone-mediated signaling pathway 200/18,866 0.010722411
   0.070827152 0.042102702 CLOCK/STRN3 2
   BP GO:0001921 positive regulation of receptor recycling 14/18,866
   0.011077589 0.070827152 0.042102702 INPP5F 1
   BP GO:0031272 regulation of pseudopodium assembly 14/18,866 0.011077589
   0.070827152 0.042102702 APC 1
   BP GO:0031958 corticosteroid receptor signaling pathway 14/18,866
   0.011077589 0.070827152 0.042102702 CLOCK 1
   BP GO:0038166 angiotensin-activated signaling pathway 14/18,866
   0.011077589 0.070827152 0.042102702 ROCK2 1
   BP GO:0048681 negative regulation of axon regeneration 14/18,866
   0.011077589 0.070827152 0.042102702 INPP5F 1
   BP GO:0051775 response to redox state 14/18,866 0.011077589 0.070827152
   0.042102702 CLOCK 1
   BP GO:0070672 response to interleukin-15 14/18,866 0.011077589
   0.070827152 0.042102702 ACSL4 1
   BP GO:1905205 positive regulation of connective tissue replacement
   14/18,866 0.011077589 0.070827152 0.042102702 ROCK2 1
   BP GO:0071383 cellular response to steroid hormone stimulus 206/18,866
   0.011345864 0.070827152 0.042102702 CLOCK/STRN3 2
   BP GO:1902905 positive regulation of supramolecular fiber organization
   208/18,866 0.011557199 0.070827152 0.042102702 ROCK2/RICTOR 2
   BP GO:0035303 regulation of dephosphorylation 209/18,866 0.011663523
   0.070827152 0.042102702 CEP192/ROCK2 2
   BP GO:0045216 cell–cell junction organization 210/18,866 0.011770283
   0.070827152 0.042102702 ROCK2/APC 2
   BP GO:0032252 secretory granule localization 15/18,866 0.011864447
   0.070827152 0.042102702 KIF5B 1
   BP GO:0070571 negative regulation of neuron projection regeneration
   15/18,866 0.011864447 0.070827152 0.042102702 INPP5F 1
   BP GO:1900037 regulation of the cellular response to hypoxia 15/18,866
   0.011864447 0.070827152 0.042102702 ROCK2 1
   BP GO:1901550 regulation of endothelial cell development 15/18,866
   0.011864447 0.070827152 0.042102702 ROCK2 1
   BP GO:1903140 regulation of establishment of the endothelial barrier
   15/18,866 0.011864447 0.070827152 0.042102702 ROCK2 1
   BP GO:1905203 regulation of connective tissue replacement 15/18,866
   0.011864447 0.070827152 0.042102702 ROCK2 1
   BP GO:0007623 circadian rhythm 218/18,866 0.01264001 0.072503172
   0.043099 ROCK2/CLOCK 2
   BP GO:0031269 pseudopodium assembly 16/18,866 0.01265072 0.072503172
   0.043099 APC 1
   BP GO:0042532 negative regulation of tyrosine phosphorylation of STAT
   protein 16/18,866 0.01265072 0.072503172 0.043099 INPP5F 1
   BP GO:0045725 positive regulation of the glycogen biosynthetic process
   16/18,866 0.01265072 0.072503172 0.043099 EPM2AIP1 1
   BP GO:2000651 positive regulation of sodium ion transmembrane
   transporter activity 16/18,866 0.01265072 0.072503172 0.043099 KIF5B 1
   BP GO:0000075 cell cycle checkpoint 219/18,866 0.012750671 0.072503172
   0.043099 CLOCK/APC 2
   BP GO:0007163 establishment or maintenance of cell polarity 220/18,866
   0.012861762 0.072503172 0.043099 ROCK2/RICTOR 2
   BP GO:0002064 epithelial cell development 221/18,866 0.012973283
   0.072503172 0.043099 ROCK2/CLOCK 2
   BP GO:0008154 actin polymerization or depolymerization 221/18,866
   0.012973283 0.072503172 0.043099 RICTOR/TWF1 2
   BP GO:0043624 cellular protein complex disassembly 224/18,866
   0.013310416 0.073527015 0.043707617 APC/TWF1 2
   BP GO:0031268 pseudopodium organization 17/18,866 0.013436409
   0.073527015 0.043707617 APC 1
   BP GO:0070875 positive regulation of glycogen metabolic process
   17/18,866 0.013436409 0.073527015 0.043707617 EPM2AIP1 1
   BP GO:0051495 positive regulation of cytoskeleton organization
   230/18,866 0.013996188 0.076062045 0.045214547 ROCK2/RICTOR 2
   BP GO:0032271 regulation of protein polymerization 231/18,866
   0.014111968 0.076165963 0.045276321 RICTOR/TWF1 2
   BP GO:0035338 long-chain fatty acyl–CoA biosynthetic process 19/18,866
   0.015006037 0.080440524 0.047817303 ACSL4 1
   BP GO:0032886 regulation of microtubule-based process 240/18,866
   0.015172907 0.080785476 0.048022358 ROCK2/APC 2
   BP GO:0003323 type B pancreatic cell development 20/18,866 0.015789976
   0.080795463 0.048028294 CLOCK 1
   BP GO:0008090 retrograde axonal transport 20/18,866 0.015789976
   0.080795463 0.048028294 KIF5B 1
   BP GO:0045019 negative regulation of a nitric oxide biosynthetic
   process 20/18,866 0.015789976 0.080795463 0.048028294 ROCK2 1
   BP GO:0097709 connective tissue replacement 20/18,866 0.015789976
   0.080795463 0.048028294 ROCK2 1
   BP GO:1902004 positive regulation of amyloid-β formation 20/18,866
   0.015789976 0.080795463 0.048028294 ROCK2 1
   BP GO:1904406 negative regulation of a nitric oxide metabolic process
   20/18,866 0.015789976 0.080795463 0.048028294 ROCK2 1
   BP GO:1902307 positive regulation of sodium ion transmembrane transport
   21/18,866 0.016573334 0.083716582 0.049764733 KIF5B 1
   BP GO:1904886 β-catenin destruction complex disassembly 21/18,866
   0.016573334 0.083716582 0.049764733 APC 1
   CC GO:0031932 TORC2 complex 12/19,559 3.13E-05 0.001788885 0.001098438
   SMG1/RICTOR 2
   CC GO:0038201 TOR complex 15/19,559 4.97E-05 0.001788885 0.001098438
   SMG1/RICTOR 2
   CC GO:0032587 ruffle membrane 95/19,559 0.002045063 0.049081508
   0.030137768 APC/TWF1 2
   CC GO:0031256 leading edge membrane 175/19,559 0.006749208 0.070207508
   0.043109873 APC/TWF1 2
   CC GO:0001726 ruffle 179/19,559 0.007050656 0.070207508 0.043109873
   APC/TWF1 2
   CC GO:0035253 ciliary rootlet 11/19,559 0.007847494 0.070207508
   0.043109873 KIF5B 1
   CC GO:0030877 β-catenin destruction complex 12/19,559 0.008558059
   0.070207508 0.043109873 APC 1
   CC GO:1990909 Wnt signalosome 12/19,559 0.008558059 0.070207508
   0.043109873 APC 1
   CC GO:0033391 chromatoid body 13/19,559 0.009268152 0.070207508
   0.043109873 CLOCK 1
   CC GO:0098554 cytoplasmic side of the endoplasmic reticulum membrane
   15/19,559 0.010686922 0.070207508 0.043109873 EPM2AIP1 1
   CC GO:0044233 mitochondria-associated endoplasmic reticulum membrane
   16/19,559 0.011395599 0.070207508 0.043109873 ACSL4 1
   CC GO:0036464 cytoplasmic ribonucleoprotein granule 233/19,559
   0.011701251 0.070207508 0.043109873 ROCK2/CLOCK 2
   CC GO:0035770 ribonucleoprotein granule 243/19,559 0.012677658
   0.070214721 0.043114302 ROCK2/CLOCK 2
   CC GO:0000242 pericentriolar material 21/19,559 0.014931918 0.076792722
   0.047153426 CEP192 1
   MF GO:0017048 Rho GTPase binding 162/18,352 0.007540672 0.065516951
   0.030425828 ROCK2/STRN3 2
   MF GO:0070016 armadillo repeat domain binding 10/18,352 0.008145489
   0.065516951 0.030425828 STRN3 1
   MF GO:0102391 decanoate-CoA ligase activity 10/18,352 0.008145489
   0.065516951 0.030425828 ACSL4 1
   MF GO:0031956 medium-chain fatty acid–CoA ligase activity 11/18,352
   0.008956623 0.065516951 0.030425828 ACSL4 1
   MF GO:0047676 arachidonate-CoA ligase activity 11/18,352 0.008956623
   0.065516951 0.030425828 ACSL4 1
   MF GO:0034595 phosphatidylinositol phosphate 5-phosphatase activity
   12/18,352 0.009767138 0.065516951 0.030425828 INPP5F 1
   MF GO:0035004 phosphatidylinositol 3-kinase activity 12/18,352
   0.009767138 0.065516951 0.030425828 IPMK 1
   MF GO:0045295 γ-catenin binding 12/18,352 0.009767138 0.065516951
   0.030425828 APC 1
   MF GO:0004467 long-chain fatty acid–CoA ligase activity 13/18,352
   0.010577034 0.065516951 0.030425828 ACSL4 1
   MF GO:0052745 inositol phosphate phosphatase activity 13/18,352
   0.010577034 0.065516951 0.030425828 INPP5F 1
   MF GO:0019902 phosphatase binding 194/18,352 0.010663145 0.065516951
   0.030425828 CEP192/STRN3 2
   MF GO:0003996 acyl-CoA ligase activity 16/18,352 0.013003014
   0.065516951 0.030425828 ACSL4 1
   MF GO:0008574 ATP-dependent microtubule motor activity,
   plus-end-directed 16/18,352 0.013003014 0.065516951 0.030425828 KIF5B 1
   MF GO:0052744 phosphatidylinositol monophosphate phosphatase activity
   18/18,352 0.014617248 0.065516951 0.030425828 INPP5F 1
   MF GO:0051010 microtubule plus-end binding 20/18,352 0.016229019
   0.065516951 0.030425828 APC 1
   MF GO:0015645 fatty acid ligase activity 22/18,352 0.017838328
   0.065516951 0.030425828 ACSL4 1
   MF GO:0017049 GTP-rho binding 22/18,352 0.017838328 0.065516951
   0.030425828 ROCK2 1
   MF GO:0050321 tau-protein kinase activity 22/18,352 0.017838328
   0.065516951 0.030425828 ROCK2 1
   MF GO:0070840 dynein complex binding 23/18,352 0.018642061 0.065516951
   0.030425828 APC 1
   MF GO:0008017 microtubule binding 265/18,352 0.019269691 0.065516951
   0.030425828 KIF5B/APC 2
   MF GO:0016405 CoA-ligase activity 26/18,352 0.021049578 0.068160538
   0.031653501 ACSL4 1
   MF GO:0003785 actin monomer binding 28/18,352 0.022651526 0.070013807
   0.032514152 TWF1 1
   MF GO:0016878 acid-thiol ligase activity 30/18,352 0.024251027
   0.071232516 0.033080116 ACSL4 1
   MF GO:0051721 protein phosphatase 2A binding 32/18,352 0.025848084
   0.071232516 0.033080116 STRN3 1
   MF GO:0052866 phosphatidylinositol phosphate phosphatase activity
   33/18,352 0.026645697 0.071232516 0.033080116 INPP5F 1
   MF GO:1990939 ATP-dependent microtubule motor activity 34/18,352
   0.027442701 0.071232516 0.033080116 KIF5B 1
   MF GO:0042162 telomeric DNA binding 36/18,352 0.029034882 0.071232516
   0.033080116 SMG1 1
   MF GO:0045296 cadherin binding 332/18,352 0.029331036 0.071232516
   0.033080116 KIF5B/TWF1 2
   MF GO:0016877 ligase activity, forming carbon–sulfur bonds 40/18,352
   0.032211948 0.075531465 0.035076532 ACSL4 1
   MF GO:0015631 tubulin binding 365/18,352 0.034916716 0.079144557
   0.036754438 KIF5B/APC 2
   MF GO:0048156 tau protein binding 45/18,352 0.036169637 0.079339848
   0.036845131 ROCK2 1
   MF GO:0070888 E-box binding 50/18,352 0.040112215 0.085238458
   0.039584423 CLOCK 1
   MF GO:0004402 histone acetyltransferase activity 55/18,352 0.044039738
   0.085703637 0.039800451 CLOCK 1
   MF GO:0017016 Ras GTPase binding 415/18,352 0.044107496 0.085703637
   0.039800451 ROCK2/STRN3 2
   MF GO:0043022 ribosome binding 57/18,352 0.045606543 0.085703637
   0.039800451 RICTOR 1
   MF GO:0061733 peptide–lysine–N-acetyltransferase activity 57/18,352
   0.045606543 0.085703637 0.039800451 CLOCK 1
   MF GO:0031267 small GTPase binding 428/18,352 0.046632861 0.085703637
   0.039800451 ROCK2/STRN3 2
   MF GO:0004674 protein serine/threonine kinase activity 435/18,352
   0.048014931 0.085921455 0.039901604 SMG1/ROCK2 2
   [85]Open in a new tab

Figure 4.

   [86]Figure 4
   [87]Open in a new tab

   (A) GO and (B) KEGG enrichment analyses of the ceRNA network.

Table 2.

   KEGG enrichment analysis of the ceRNA network.
   ID Description BgRatio p-Value P. Adjust Q-Value Gene ID Count
   hsa00562 inositol phosphate metabolism 73/8104 0.002765887 0.128071623
   0.109375964 IPMK/INPP5F 2
   hsa04070 phosphatidylinositol signaling system 97/8104 0.004832891
   0.128071623 0.109375964 IPMK/INPP5F 2
   hsa04728 dopaminergic synapse 132/8104 0.008794936 0.155377197
   0.132695521 CLOCK/KIF5B 2
   hsa04310 Wnt signaling pathway 166/8104 0.013659929 0.180994061
   0.154572882 ROCK2/APC 2
   hsa00061 fatty acid biosynthesis 18/8104 0.019823143 0.202227639
   0.172706822 ACSL4 1
   hsa04810 regulation of the actin cytoskeleton 218/8104 0.022893695
   0.202227639 0.172706822 ROCK2/APC 2
   hsa05132 Salmonella infection 249/8104 0.029353631 0.222248917
   0.18980543 ROCK2/KIF5B 2
   hsa04710 circadian rhythm 31/8104 0.033921832 0.224732137 0.191926155
   CLOCK 1
   hsa04216 ferroptosis 41/8104 0.044644012 0.24791126 0.211721632 ACSL4 1
   hsa00071 fatty acid degradation 43/8104 0.046775709 0.24791126
   0.211721632 ACSL4 1
   [88]Open in a new tab

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
   [[89]19,[90]20,[91]21,[92]22,[93]23,[94]24]. Moreover, according to the
   ceRNA theory [[95]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 [[96]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 [[97]13].
   The lncRNA HOTAIR can alleviate the progression of RA by targeting
   miR-138 and inhibiting the NF-κB pathway [[98]19]. The lncRNA GAS5 can
   alleviate RA by regulating the miR-222-3p/Sirt1 signaling axis
   [[99]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
   [[100]32,[101]33,[102]34,[103]35,[104]36,[105]37,[106]38,[107]39,[108]4
   0,[109]41,[110]42,[111]43,[112]44,[113]45,[114]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
   [[115]47,[116]48,[117]49,[118]50,[119]51,[120]52,[121]53,[122]54,[123]5
   5,[124]56,[125]57,[126]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 [127]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 [[128]59]. In addition, a study has shown that moxibustion can also
   produce similar effects by inhibiting the mTOR pathway [[129]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 [[130]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 [[131]62] have been regarded as target pathways for treating RA
   in many studies [[132]63,[133]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 [[134]65,[135]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 [[136]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 ([137]Figure 4), potential dopamine
   functions in RA have been widely considered in recent decades
   [[138]67]. Dopamine can indirectly affect the immune system through
   prolactin [[139]68,[140]69,[141]70] or can directly affect immune cells
   through the dopamine receptors (DR) expressed by immune cells
   [[142]71]. The effects of dopamine are exerted on the basis of the
   dose-dependent differences and different states (activated and
   nonactivated) of cells [[143]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 [[144]72]
   and the number of D3DR+ mast cells is negatively correlated with the
   progression of the disease [[145]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 [[146]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. [[147]74]
   and Erb et al. [[148]75]; bromocriptine, a D2-like agonist, by McMurray
   [[149]76] and Figueroa et al. [[150]77]; and quinagolide, a D2-like
   agonist, by Eijsbouts et al. [[151]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. [[152]79,[153]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 [[154]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 [[155]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 [[156]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 [[157]83]. However, in this study ([158]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
   ([159]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 [[160]84]. In addition, the NF-κB pathway has been
   shown to interact with the Wnt pathway to mediate inflammatory
   responses [[161]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
   [[162]74,[163]75,[164]86,[165]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 [[166]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
   [[167]59,[168]60,[169]61,[170]62,[171]63,[172]64,[173]65,[174]66,[175]6
   7,[176]68,[177]69,[178]70,[179]71,[180]72,[181]73,[182]74,[183]75,[184]
   76,[185]77,[186]78,[187]79,[188]80,[189]81,[190]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.

Acknowledgments