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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">855557</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2022.855557</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Identification of Tissue-Specific Expressed Hub Genes and Potential Drugs in Rheumatoid Arthritis Using Bioinformatics Analysis</article-title>
<alt-title alt-title-type="left-running-head">Xing et&#x20;al.</alt-title>
<alt-title alt-title-type="right-running-head">Rheumatoid Arthritis Using Bioinformatics Analysis</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Xing</surname>
<given-names>Xuewu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xia</surname>
<given-names>Qun</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Gong</surname>
<given-names>Baoqi</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Shen</surname>
<given-names>Zhongyang</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Yingze</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff5">
<sup>5</sup>
</xref>
<xref ref-type="aff" rid="aff6">
<sup>6</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1636011/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Orthopaedics</institution>, <institution>Tianjin First Central Hospital</institution>, <addr-line>Tianjin</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>School of Medicine</institution>, <institution>Nankai University</institution>, <addr-line>Tianjin</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Rheumatology</institution>, <institution>Tianjin First Central Hospital</institution>, <addr-line>Tianjin</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Transplant Surgery</institution>, <institution>Tianjin First Central Hospital</institution>, <addr-line>Tianjin</addr-line>, <country>China</country>
</aff>
<aff id="aff5">
<sup>5</sup>
<institution>Department of Orthopaedic Surgery of Hebei Province</institution>, <institution>Third Hospital of Hebei Medical University</institution>, <addr-line>Shijiazhuang</addr-line>, <country>China</country>
</aff>
<aff id="aff6">
<sup>6</sup>
<institution>Chinese Academy of Engineering</institution>, <addr-line>Beijing</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1188392/overview">Rajesh Pandey</ext-link>, CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), India</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/801391/overview">Sergey V. Kozyrev</ext-link>, Uppsala University, Sweden</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1339448/overview">Xueyuan Jiang</ext-link>, Merck, United&#x20;States</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Yingze Zhang, <email>yzzhang2022@foxmail.com</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to RNA, a section of the journal Frontiers in Genetics</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>18</day>
<month>03</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>13</volume>
<elocation-id>855557</elocation-id>
<history>
<date date-type="received">
<day>15</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>18</day>
<month>02</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Xing, Xia, Gong, Shen and Zhang.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Xing, Xia, Gong, Shen and Zhang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these&#x20;terms.</p>
</license>
</permissions>
<abstract>
<p>
<bold>Background:</bold> Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted&#x20;drugs.</p>
<p>
<bold>Methods:</bold> Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM&#x2013;DEG pairs and ceRNA network containing key DEMs were established by Cytoscape.</p>
<p>
<bold>Results:</bold> We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-&#x3ba;B pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3&#x20;tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA&#x2013;miRNA&#x2013;mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB.</p>
<p>
<bold>Conclusion:</bold> This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for&#x20;RA.</p>
</abstract>
<kwd-group>
<kwd>rheumatoid arthritis</kwd>
<kwd>GEO database</kwd>
<kwd>tissue specific</kwd>
<kwd>hub gene</kwd>
<kwd>competing endogenous RNA</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by hyperplastic synovium, invasive synovitis, and progressive joint damage (<xref ref-type="bibr" rid="B40">Scott et&#x20;al., 2010</xref>). The incidence of RA is approximately 1% in the adult population, mainly affecting middle-aged to elderly women (<xref ref-type="bibr" rid="B41">Stephenson et&#x20;al., 2018</xref>). RA is the leading cause of disability worldwide and imposes a significant public health and economic burden (<xref ref-type="bibr" rid="B39">Schmidt et&#x20;al., 2020</xref>). The etiology of RA is complex and multifactorial (<xref ref-type="bibr" rid="B32">McInnes and Schett, 2011</xref>). Despite there have been major advances in the understanding of RA development, the pathogenesis of RA remains incompletely elucidated.</p>
<p>Microarray analyses has improved the ability of human to study the pathogenesis of diseases, increasingly being used to explore disease epigenetics and to screen for effective biomarkers for disease diagnosis and treatment (<xref ref-type="bibr" rid="B22">Kulasingam and Diamandis, 2008</xref>). Although several genes, such as HLA-DR, PTPTN22, CTLA-4, and PADI4, have been identified as genetic factors that contribute to RA susceptibility, we still don&#x2019;t know the exact mechanisms by which these genes trigger RA (<xref ref-type="bibr" rid="B33">Moran-Moguel et&#x20;al., 2018</xref>). Therefore, we can elucidate the pathogenesis of RA by identifying potential biomarkers and potential therapeutic targets. In addition, the competing endogenous RNA (ceRNA) hypothesis could facilitate the elucidation of molecular mechanisms underlying disease progression (<xref ref-type="bibr" rid="B37">Salmena et&#x20;al., 2011</xref>). Tissue-specific expressed hub genes and regulatory networks of disease may be screened through transcriptome microarray and bioinformatic analysis (<xref ref-type="bibr" rid="B8">Cheng et&#x20;al., 2021</xref>).</p>
<p>Del Rey et&#x20;al. conducted transcriptional analysis of normal and pathological synoviocytes using microarray expression profiling (<xref ref-type="bibr" rid="B12">Del Rey et&#x20;al., 2012</xref>). Georgel P et&#x20;al. used high throughput expression analysis to evaluate the overall miRNA expression level in fibroblast-like synoviocytes (FLS) isolated from RA patients in comparison with FLS from osteoarthritic (OA) patients (<xref ref-type="bibr" rid="B28">Liu and Ren, 2020</xref>). Bi et&#x20;al. assessed the differential expression profiles of lncRNA in FLSs between the RA group and the healthy control groups (<xref ref-type="bibr" rid="B1">Bi et&#x20;al., 2019</xref>). In the present study, we downloaded the expression profiles of the above three different types of RNA from GEO database to screen the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples. Hub genes, tissue-specific expressed genes, RNA regulatory networks, and novel anti-RA potential drugs were obtained by comprehensive analysis. This work provides a deeper understanding of the molecular mechanism underlying RA development and reveals novel biomarkers for disease diagnosis and potential drug for disease therapy.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and Methods</title>
<sec id="s2-1">
<title>Datasets Collection</title>
<p>Three human microarray datasets involving fibroblast-like synoviocytes (FLS) were obtained by searching NCBI&#x2019;s Gene Expression Omnibus (GEO) database (<ext-link ext-link-type="uri" xlink:href="http://www.ncbi.nlm">http://www.ncbi.nlm</ext-link>. nih. gov/geo/). The mRNA, miRNA and lncRNA expression profiles were procured from GSE29746, GSE72564 and GSE128813 respectively, the description of these data sets was detailed below (<xref ref-type="table" rid="T1">Table&#x20;1</xref>).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Details of the 3 human microarray datasets.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left"/>
<th align="center">GSE29746</th>
<th align="center">GSE72564</th>
<th align="center">GSE128813</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Expression profiling</td>
<td align="center">mRNA</td>
<td align="center">miRNA</td>
<td align="center">lncRNA</td>
</tr>
<tr>
<td align="left">Public on</td>
<td align="center">25 Oct 2011</td>
<td align="center">01 Sep 2015</td>
<td align="center">08 Jan 2020</td>
</tr>
<tr>
<td align="left">Organization</td>
<td align="center">Spain</td>
<td align="center">France</td>
<td align="center">China</td>
</tr>
<tr>
<td align="left">Samples of RA group</td>
<td align="center">9</td>
<td align="center">4</td>
<td align="center">3</td>
</tr>
<tr>
<td align="left">Samples of control group</td>
<td align="center">11</td>
<td align="center">4</td>
<td align="center">3</td>
</tr>
<tr>
<td align="left">Platforms</td>
<td align="center">GPL4133 Agilent</td>
<td align="center">GPL20870 Qiagen</td>
<td align="center">GPL21827 Agilent</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>RA, rheumatoid arthritis; OA, osteoarthritis.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2-2">
<title>Authentication of DEG, DEM, and DEL</title>
<p>Through GEO2R online tool, we obtained differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs). GEO2R is an interactive online tool that can be used to compare multiple expression data and then identify differentially expressed profiles. We selected differentially expressed RNAs according to the following criteria: <italic>p</italic>&#x20;&#x3c; 0.05 and &#x7c;log2 (fold change) &#x7c;&#x2265;1.</p>
</sec>
<sec id="s2-3">
<title>Authentication of the Tissue-specific Expressed Genes</title>
<p>We analyzed the distribution of DEGs using an online tool BioGPS (<ext-link ext-link-type="uri" xlink:href="http://biogps.org/">http://biogps.org/</ext-link>) which was created as a centralized gene-annotation portal for clustering distributed gene (<xref ref-type="bibr" rid="B43">Wu et&#x20;al., 2009</xref>). Tissue-specific genes should qualify the following criteria: 1) the expression level of transcripts that mapped to a single organ system was &#x3e;10&#x20;times the median, and 2) the highest expression level was more than half as high as the second highest level (<xref ref-type="bibr" rid="B8">Cheng et&#x20;al., 2021</xref>).</p>
</sec>
<sec id="s2-4">
<title>Functional Enrichment Analysis</title>
<p>To identify the function of DEGs, the enrichment analysis was conducted by DAVID (version 6.8) using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database (<xref ref-type="bibr" rid="B17">Huang et&#x20;al., 2007</xref>). We defined enriched functions and pathways using a cutoff of <italic>p</italic>&#x20;&#x3c;&#x20;0.05.</p>
</sec>
<sec id="s2-5">
<title>Construction and Analysis of Protein&#x2013;Protein Interaction (PPI) Network</title>
<p>A PPI network was constructed for the DEGs using STRING (<ext-link ext-link-type="uri" xlink:href="https://string-db.org/">https://string-db.org/</ext-link>) with a filter condition (interaction score&#x3e;0.4), a online tool of known, and predicted PPIs. We use the Cytoscape to visualize the network. In order to screen the important pathways related to the development of RA, we constructed an interaction network between DEGs and the signaling pathway using the ClueGO plug-in of Cytoscape platform. We applied MCODE plug-in of Cytoscape to identify hub genes of significant modules with following criteria: MCODE scores &#x3e;5, degree cut-off &#x3d; 2, node score cut-off &#x3d; 0.2, Max depth &#x3d; 100, and k-score &#x3d;&#x20;2.</p>
</sec>
<sec id="s2-6">
<title>CMap Analysis</title>
<p>We run Connectivity Map (CMap) to identify novel anti-RA potential drugs. Through the database which is available online (<ext-link ext-link-type="uri" xlink:href="http://www.broadinstitute.org/cmap/">http://www.broadinstitute.org/cmap/</ext-link>), we can get the similarity between each researcher&#x2019;s DEGs and 7,056 gene expression profiles caused by 1,309 bioactive compounds (<xref ref-type="bibr" rid="B17">Huang et&#x20;al., 2007</xref>). The similarity is assessed by the &#x2018;connectivity score&#x2019;. The connectivity score ranges from &#x2212;1 to &#x2b;1; a positive score means a promoted effect, while a negative score means an inhibited effect (<xref ref-type="bibr" rid="B36">Peng et&#x20;al., 2020</xref>).</p>
</sec>
<sec id="s2-7">
<title>Construction of kDEM-DEG Pairs</title>
<p>The potential microRNA binding to DEGs were predicted using TargetScan (<ext-link ext-link-type="uri" xlink:href="http://www.targetscan.org/vert_72/">http://www.targetscan.org/vert_72/</ext-link>). The key differentially expressed miRNAs (kDEM) were obtained by the intersection between predicted miRNAs and differentially expressed miRNAs found from the GSE72564 dataset. The kDEM&#x2013;DEG pairs were constructed using Cytoscape.</p>
</sec>
<sec id="s2-8">
<title>Establishment of the ceRNA Network</title>
<p>The miRcode database (<ext-link ext-link-type="uri" xlink:href="http://www.mircode.org/">http://www.mircode.org/</ext-link>) was used to predict miRNA that can interact with DELs. We used Cytoscape to construct the ceRNA network by selecting lncRNA&#x2013;miRNA&#x2013;mRNA interaction that contain key&#x20;DEMs.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec id="s3-1">
<title>Identification of DEGs</title>
<p>We reanalyzed the GEO data of GSE29746 in order to compare the expression pattern of differential genes between pathological synovial tissues from 9 rheumatoid arthritis (RA) patients and normal synovial tissues from 11 sex and age matched osteoarthritis (OA) patients. A total of 418 DEGs were obtained, comprising 206 upregulated genes and 212 downregulated genes. The data of DEGs was visualized as a volcano plot (<xref ref-type="fig" rid="F1">Figure&#x20;1A</xref>), The top 10 DEGs were displayed in a heatmap (<xref ref-type="fig" rid="F1">Figure&#x20;1B</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Expression profiles of differentially expressed mRNAs (DEGs) in RA. <bold>(A)</bold> Volcano plot of DEGs (red: upregulated DEGs. green: downregulated DEGs.) <bold>(B)</bold> Heatmap of the top 10 upregulated DEGs and the top 10 downregulated DEGs (red: high expression; blue: low expression).</p>
</caption>
<graphic xlink:href="fgene-13-855557-g001.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>Authentication of the tissue-Specific Expressed Genes</title>
<p>We identified 64&#x20;tissue-specific expressed genes using BioGPS (<xref ref-type="table" rid="T2">Table&#x20;2</xref>). The system with the greatest distribution of tissue-specific expressed genes is the hematologic/immune system (20/46, 31.25%), followed by the digestive system (11), nervous system (10), endocrine system (7), genital system (6), respiratory system (6), urinary system (2), circulatory system (1) and motor system&#x20;(1).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Distribution of tissue specific expressed genes identified by BioGPS.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">system (counts)</th>
<th align="center">Tissue</th>
<th align="center">Genes</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="9" align="left">Hematologic/Immune (20)</td>
<td align="left">CD56 &#x2b; NK cells</td>
<td align="left">TRPV2, PTGER2, ADGRG1, PREX1, NKG7</td>
</tr>
<tr>
<td align="left">BDCA4&#x2b;dendritic cells</td>
<td align="left">LAMP5, CUX2</td>
</tr>
<tr>
<td align="left">CD19 &#x2b; B&#x20;cells</td>
<td align="left">VPREB3, BIRC3</td>
</tr>
<tr>
<td align="left">CD14 &#x2b; monocytes</td>
<td align="left">ADGRE1, TNFAIP8L2</td>
</tr>
<tr>
<td align="left">CD33 &#x2b; myeloid</td>
<td align="left">CCR1, TLR4, THEMIS2</td>
</tr>
<tr>
<td align="left">721&#xa0;B lymphoblasts</td>
<td align="left">IFI44L, SPAG5</td>
</tr>
<tr>
<td align="left">CD4&#x2b;T&#x20;cells</td>
<td align="left">TRABD2A</td>
</tr>
<tr>
<td align="left">CD8&#x2b;T&#x20;cells</td>
<td align="left">TBC1D10C</td>
</tr>
<tr>
<td align="left">whole blood</td>
<td align="left">CLEC2B, KCNJ15</td>
</tr>
<tr>
<td rowspan="4" align="left">Digestive (11)</td>
<td align="left">colorectal adenocarcinoma</td>
<td align="left">HOXB7, MSX1, NGFR, KRT80, RBP1</td>
</tr>
<tr>
<td align="left">salivary gland</td>
<td align="left">CST2, OPRPN</td>
</tr>
<tr>
<td align="left">small intestine</td>
<td align="left">SLC5A1</td>
</tr>
<tr>
<td align="left">liver</td>
<td align="left">HRG, HAAO, PLG</td>
</tr>
<tr>
<td rowspan="4" align="left">Nervous (10)</td>
<td align="left">fetal brain</td>
<td align="left">STMN2, LDLRAD4, DLX1</td>
</tr>
<tr>
<td align="left">whole brain</td>
<td align="left">CRYM, BRINP1, MAPRE3</td>
</tr>
<tr>
<td align="left">prefrontal cortex</td>
<td align="left">SHC3, CERCAM</td>
</tr>
<tr>
<td align="left">retina</td>
<td align="left">FRZB, AOC2</td>
</tr>
<tr>
<td rowspan="3" align="left">Endocrine (7)</td>
<td align="left">pineal</td>
<td align="left">BEX5, CDH10, IGSF21, ASMT</td>
</tr>
<tr>
<td align="left">pancreatic islet</td>
<td align="left">CRP</td>
</tr>
<tr>
<td align="left">thyroid</td>
<td align="left">DUOX2, ID4</td>
</tr>
<tr>
<td rowspan="4" align="left">Genital (6)</td>
<td align="left">testis germ cells</td>
<td align="left">CHODL</td>
</tr>
<tr>
<td align="left">testis intersitial</td>
<td align="left">SPATA8, LYZL6</td>
</tr>
<tr>
<td align="left">uterus</td>
<td align="left">OGN</td>
</tr>
<tr>
<td align="left">uterus corpus</td>
<td align="left">LEFTY2, MMP10</td>
</tr>
<tr>
<td rowspan="2" align="left">Respiratory (6)</td>
<td align="left">lung</td>
<td align="left">NEDD9, SFTPB, HEY1</td>
</tr>
<tr>
<td align="left">bronchial epithelial cells</td>
<td align="left">PHLDA1, AMIGO2, FZD6</td>
</tr>
<tr>
<td align="left">Urinary (2)</td>
<td align="left">kidney</td>
<td align="left">FMO1, CRYAA</td>
</tr>
<tr>
<td align="left">Circulatory (1)</td>
<td align="left">heart</td>
<td align="left">HHATL</td>
</tr>
<tr>
<td align="left">Motor (1)</td>
<td align="left">skeletal muscle</td>
<td align="left">ACTN3</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>CD, cluster differentiation.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3-3">
<title>GO Term and KEGG Pathway Enrichment Analysis of DEGs</title>
<p>GO analysis showed that the biological processes (BP) of DEGs focused primarily on synapse assembly, synapse organization, positive regulation of neuron differentiation, cerebral cortex neuron differentiation, endocardial cushion development, positive regulation of neurogenesis, skeletal muscle organ development, negative regulation of blood coagulation, pallium development, and cell junction assembly (<xref ref-type="fig" rid="F2">Figures 2A,B</xref>). The main cellular components (CC) include cell-cell junction, collagen-containing extracellular matrix, basement membrane, adherent junction, integral component of postsynaptic specialization membrane, integral component of presynaptic active zone membrane, intrinsic component of postsynaptic specialization membrane, MHC class II protein complex, synaptic membrane, and clathrin-coated endocytic vesicle (<xref ref-type="fig" rid="F2">Figure&#x20;2A</xref>). The molecular functions (MF) include actin monomer binding, extracellular matrix structural constituent, channel activity, passive transmembrane transporter activity, ion channel activity, ligand-gated ion channel activity, ligand-gated channel activity, cation channel activity, protein binding involved in heterotypic cell-cell adhesion, and endopeptidase inhibitor activity (<xref ref-type="fig" rid="F2">Figure&#x20;2A</xref>). Genes are mainly involved in the KEGG pathway termed Amoebiasis, Cell adhesion molecules (CAMs), Staphylococcus aureus infection, Asthma, Rheumatoid arthritis, Toxoplasmosis, Glycosphingolipid biosynthesis-ganglio series, Allograft rejection, Leishmaniasis, and Tryptophan metabolism (<xref ref-type="fig" rid="F2">Figure&#x20;2C</xref>; <xref ref-type="table" rid="T3">Table&#x20;3</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>GO and KEGG pathway enrichment analyses of differentially expressed mRNAs (DEGs). <bold>(A)</bold> Top 10 enriched GO terms of the DEGs. <bold>(B)</bold> cnetplot showing the top 10 enriched biological processes of DEGs <bold>(C)</bold>. The bubble plot showing top 10 enriched KEGG pathways of DEGs.</p>
</caption>
<graphic xlink:href="fgene-13-855557-g002.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Enrichment analysis of KEGG pathway.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">KEGG ID</th>
<th align="center">Term</th>
<th align="center">GeneRatio</th>
<th align="center">PValue</th>
<th align="center">Genes</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">hsa05146</td>
<td align="left">Amoebiasis</td>
<td align="char" char="/">7/94</td>
<td align="char" char=".">0.001414621</td>
<td align="left">ACTN3/LAMA1/GNA14/TLR4/COL4A1/SERPINB4/SERPINB2</td>
</tr>
<tr>
<td align="left">hsa04514</td>
<td align="left">Cell adhesion molecules (CAMs)</td>
<td align="char" char="/">7/94</td>
<td align="char" char=".">0.005757522</td>
<td align="left">HLA-DRB5/NCAM2/HLA-DQA2/HLA-DRB4/NFASC/CDH15/CDH2</td>
</tr>
<tr>
<td align="left">hsa05150</td>
<td align="left">Staphylococcus aureus infection</td>
<td align="char" char="/">4/94</td>
<td align="char" char=".">0.011840374</td>
<td align="left">HLA-DRB5/HLA-DQA2/HLA-DRB4/PLG</td>
</tr>
<tr>
<td align="left">hsa05310</td>
<td align="left">Asthma</td>
<td align="char" char="/">3/94</td>
<td align="char" char=".">0.012784974</td>
<td align="left">HLA-DRB5/HLA-DQA2/HLA-DRB4</td>
</tr>
<tr>
<td align="left">hsa05323</td>
<td align="left">Rheumatoid arthritis</td>
<td align="char" char="/">5/94</td>
<td align="char" char=".">0.015329475</td>
<td align="left">HLA-DRB5/TLR4/HLA-DQA2/ATP6V1C2/HLA-DRB4</td>
</tr>
<tr>
<td align="left">hsa05145</td>
<td align="left">Toxoplasmosis</td>
<td align="char" char="/">6/94</td>
<td align="char" char=".">0.019005793</td>
<td align="left">LAMA1/HLA-DRB5/BIRC3/TLR4/HLA-DQA2/HLA-DRB4</td>
</tr>
<tr>
<td align="left">hsa00604</td>
<td align="left">Glycosphingolipid biosynthesis - ganglio series</td>
<td align="char" char="/">2/94</td>
<td align="char" char=".">0.023088868</td>
<td align="left">ST6GALNAC3/ST6GALNAC5</td>
</tr>
<tr>
<td align="left">hsa05330</td>
<td align="left">Allograft rejection</td>
<td align="char" char="/">3/94</td>
<td align="char" char=".">0.023727967</td>
<td align="left">HLA-DRB5/HLA-DQA2/HLA-DRB4</td>
</tr>
<tr>
<td align="left">hsa05140</td>
<td align="left">Leishmaniasis</td>
<td align="char" char="/">4/94</td>
<td align="char" char=".">0.028621472</td>
<td align="left">HLA-DRB5/TLR4/HLA-DQA2/HLA-DRB4</td>
</tr>
<tr>
<td align="left">hsa00380</td>
<td align="left">Tryptophan metabolism</td>
<td align="char" char="/">3/94</td>
<td align="char" char=".">0.028807929</td>
<td align="left">ASMT/IL4I1/HAAO</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>KEGG , kyoto encyclopedia of genes and genomes.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3-4">
<title>Construction and Analysis of PPI Network</title>
<p>Based on the identified DEGs and the STRING database, we constructed a PPI network containing 364 nodes and 361 edges (<xref ref-type="fig" rid="F3">Figure&#x20;3A</xref>). We found out that NF-&#x3ba;B pathway may be involved in the pathogenesis of RA through Gene-pathway network constructed by The ClueGO plugin of Cytoscape (<xref ref-type="fig" rid="F3">Figure&#x20;3B</xref>). A total of 13 important module were generated via using MCODE plugin in Cytoscape, and 10 hub genes were obtained (<xref ref-type="table" rid="T4">Table&#x20;4</xref>).</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Construction and analysis of protein-protein interaction (PPI) network <bold>(A)</bold> PPI network of differentially expressed mRNAs (DEGs) constructed with STRING databases <bold>(B)</bold> Gene-pathway network related to the development of Rheumatoid arthritis (RA).</p>
</caption>
<graphic xlink:href="fgene-13-855557-g003.tif"/>
</fig>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Hub gene obtained by MCODE plug-in of Cytoscape.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">No</th>
<th align="center">Gene symbol</th>
<th align="center">Log FC</th>
<th align="center">
<italic>p</italic> Value</th>
<th align="center">Full name</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">1</td>
<td align="left">MMP10</td>
<td align="char" char=".">2.739087238</td>
<td align="char" char=".">0.026051857</td>
<td align="left">matrix metallopeptidase 10</td>
</tr>
<tr>
<td align="left">2</td>
<td align="left">TRIM3</td>
<td align="char" char=".">&#x2212;1.3827721</td>
<td align="char" char=".">0.03985551</td>
<td align="left">tripartite motif containing 3</td>
</tr>
<tr>
<td align="left">3</td>
<td align="left">THEMIS2</td>
<td align="char" char=".">1.24876953</td>
<td align="char" char=".">0.0083544</td>
<td align="left">thymocyte selection associated family member 2</td>
</tr>
<tr>
<td align="left">4</td>
<td align="left">DLX2</td>
<td align="char" char=".">&#x2212;1.1895073</td>
<td align="char" char=".">0.01692604</td>
<td align="left">distal-less homeobox 2</td>
</tr>
<tr>
<td align="left">5</td>
<td align="left">SPAG5</td>
<td align="char" char=".">1.03248394</td>
<td align="char" char=".">0.0068909</td>
<td align="left">sperm associated antigen 5</td>
</tr>
<tr>
<td align="left">6</td>
<td align="left">MIP</td>
<td align="char" char=".">&#x2212;1.1818731</td>
<td align="char" char=".">0.00050311</td>
<td align="left">major intrinsic protein of lens fiber</td>
</tr>
<tr>
<td align="left">7</td>
<td align="left">ASB2</td>
<td align="char" char=".">1.20540696</td>
<td align="char" char=".">0.0045946</td>
<td align="left">ankyrin repeat and SOCS box containing 2</td>
</tr>
<tr>
<td align="left">8</td>
<td align="left">CXCL2</td>
<td align="char" char=".">1.18019999</td>
<td align="char" char=".">0.01749176</td>
<td align="left">C-X-C motif chemokine ligand 2</td>
</tr>
<tr>
<td align="left">9</td>
<td align="left">CUX2</td>
<td align="char" char=".">&#x2212;1.9375622</td>
<td align="char" char=".">0.00088542</td>
<td align="left">cut like homeobox 2</td>
</tr>
<tr>
<td align="left">10</td>
<td align="left">FRZB</td>
<td align="char" char=".">&#x2212;1.5413208</td>
<td align="char" char=".">0.00284952</td>
<td align="left">frizzled-related protein</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SOCS, suppressor of cytokine signalling.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3-5">
<title>ROC Curve of the 3&#x20;Tissue-Specific Expressed Hub Genes</title>
<p>Furthermore, we analyzed the performance of 3&#x20;tissue-specific expressed hub genes in diagnosing RA by means of receiver operating characteristic (ROC) curve analysis in SPSS 23.0 software. The area under the ROC curve (AUC) was calculated to indicate diagnostic efficiency and predictive accuracy (<xref ref-type="bibr" rid="B8">Cheng et&#x20;al., 2021</xref>). These 3&#x20;tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with OA samples. Specifically, SPAG5 showed the highest diagnostic performance (AUC: 0.867) in the RA samples, closely followed by CUX2 (AUC: 0.811), THEMIS2 ranks last (AUC: 0.778) (<xref ref-type="fig" rid="F4">Figure&#x20;4</xref>). Based on the above data, these 3&#x20;tissue-specific expressed hub genes may serve as potential biomarkers for RA diagnosis.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>ROC curve of the 3 specifically expressed hub genes in Rheumatoid arthritis (RA) samples. AUC area under the ROC&#x20;curve.</p>
</caption>
<graphic xlink:href="fgene-13-855557-g004.tif"/>
</fig>
</sec>
<sec id="s3-6">
<title>Identification of Potential Targeted Drugs by CMap Analysis</title>
<p>We obtained a list of compounds after uploading DEGs tags to the CMap database. The top five compounds with the highest scores which may be potential drugs for RA are shown in <xref ref-type="table" rid="T5">Table&#x20;5</xref>.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>The top five compounds with the highest scores in the CMap analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">CMap name</th>
<th align="center">Dose</th>
<th align="center">Cell line</th>
<th align="center">Score</th>
<th align="center">Instance ID</th>
<th align="center">Description</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Troleandomycin</td>
<td align="center">5&#xa0;&#xb5;M</td>
<td align="center">HL60</td>
<td align="char" char=".">&#x2212;1</td>
<td align="char" char=".">1,965</td>
<td align="left">inhibitors of CYP450 monooxygenases</td>
</tr>
<tr>
<td align="left">Levodopa</td>
<td align="center">20&#xa0;&#xb5;M</td>
<td align="center">HL60</td>
<td align="char" char=".">&#x2212;0.998</td>
<td align="char" char=".">1,972</td>
<td align="left">the levorotatory form of dopa used in Parkinson&#x2019;s disease</td>
</tr>
<tr>
<td align="left">Trichostatin A</td>
<td align="center">100&#xa0;nM</td>
<td align="center">HL60</td>
<td align="char" char=".">&#x2212;0.971</td>
<td align="char" char=".">2,949</td>
<td align="left">HDAC inhibitor</td>
</tr>
<tr>
<td align="left">LY-294002</td>
<td align="center">10&#xa0;&#xb5;M</td>
<td align="center">MCF7</td>
<td align="char" char=".">&#x2212;0.952</td>
<td align="char" char=".">1,074</td>
<td align="left">MTOR inhibitor</td>
</tr>
<tr>
<td align="left">Levamisole</td>
<td align="center">17&#xa0;&#xb5;M</td>
<td align="center">HL60</td>
<td align="char" char=".">&#x2212;0.951</td>
<td align="char" char=".">1,410</td>
<td align="left">an anthelmintic drug C<sub>11</sub>H<sub>12</sub>N<sub>2</sub>S</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>CYP450, Cytochrome P450; HDAC, histone deacetylase; MTOR, mammalian target of rapamycin.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3-7">
<title>Identification of DEMs and Construction of DEM-DEG Pairs</title>
<p>A total of 23 miRNAs were identified as DEMs, of which 17 miRNAs were upregulated and 6 miRNAs were downregulated (<xref ref-type="table" rid="T6">Table&#x20;6</xref>). A total of 147 miRNAs were predicted to combine with the DEGs. A total of five key DEMs were procured (<xref ref-type="fig" rid="F5">Figure&#x20;5A</xref>), of which four were up-regulated, namely, miR-30c-2-3p, miR-20b-3p, miR-26a-1-3p, miR-218-5p, and miR-496 was the only down-regulated one. The kDEM&#x2013;DEG pair is shown in <xref ref-type="fig" rid="F5">Figure&#x20;5B</xref>.</p>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>differentially expressed miRNAs (DEMs) obtained from the GSE72564.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">ID</th>
<th align="center">adj.P.Val</th>
<th align="center">P.Value</th>
<th align="center">t</th>
<th align="center">B</th>
<th align="center">logFC</th>
<th align="center">miRNA_ID</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">846</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.00181</td>
<td align="char" char=".">5.08274</td>
<td align="char" char=".">&#x2212;0.98</td>
<td align="char" char=".">1.5375</td>
<td align="center">miR-670-5p</td>
</tr>
<tr>
<td align="left">626</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.00365</td>
<td align="char" char=".">4.44126</td>
<td align="char" char=".">&#x2212;1.51</td>
<td align="char" char=".">2.0325</td>
<td align="center">miR-26a-1-3p</td>
</tr>
<tr>
<td align="left">958</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.00745</td>
<td align="char" char=".">3.83781</td>
<td align="char" char=".">&#x2212;2.08</td>
<td align="char" char=".">1.895</td>
<td align="center">miR-2116-5p</td>
</tr>
<tr>
<td align="left">63</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02222</td>
<td align="char" char=".">2.99059</td>
<td align="char" char=".">&#x2212;2.99</td>
<td align="char" char=".">1.1625</td>
<td align="center">miR-190a-5p</td>
</tr>
<tr>
<td align="left">101</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02336</td>
<td align="char" char=".">2.95362</td>
<td align="char" char=".">&#x2212;3.03</td>
<td align="char" char=".">1.5025</td>
<td align="center">miR-548b-3p</td>
</tr>
<tr>
<td align="left">119</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02551</td>
<td align="char" char=".">2.88896</td>
<td align="char" char=".">&#x2212;3.11</td>
<td align="char" char=".">1.285</td>
<td align="center">miR-579-3p</td>
</tr>
<tr>
<td align="left">387</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02961</td>
<td align="char" char=".">2.78028</td>
<td align="char" char=".">&#x2212;3.24</td>
<td align="char" char=".">1.7775</td>
<td align="center">miR-1305</td>
</tr>
<tr>
<td align="left">408</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02995</td>
<td align="char" char=".">2.77201</td>
<td align="char" char=".">&#x2212;3.25</td>
<td align="char" char=".">1.6525</td>
<td align="center">miR-30c-3p</td>
</tr>
<tr>
<td align="left">620</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.03323</td>
<td align="char" char=".">2.69675</td>
<td align="char" char=".">&#x2212;3.34</td>
<td align="char" char=".">1.115</td>
<td align="center">miR-20b-3p</td>
</tr>
<tr>
<td align="left">797</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.03394</td>
<td align="char" char=".">2.68158</td>
<td align="char" char=".">&#x2212;3.35</td>
<td align="char" char=".">1.185</td>
<td align="center">miR-4262</td>
</tr>
<tr>
<td align="left">267</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.0345</td>
<td align="char" char=".">2.66977</td>
<td align="char" char=".">&#x2212;3.37</td>
<td align="char" char=".">1.165</td>
<td align="center">miR-502-3p</td>
</tr>
<tr>
<td align="left">666</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.0354</td>
<td align="char" char=".">2.65136</td>
<td align="char" char=".">&#x2212;3.39</td>
<td align="char" char=".">1.425</td>
<td align="center">miR-1258</td>
</tr>
<tr>
<td align="left">508</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.03672</td>
<td align="char" char=".">2.62497</td>
<td align="char" char=".">&#x2212;3.42</td>
<td align="char" char=".">2.1575</td>
<td align="center">miR-708-5p</td>
</tr>
<tr>
<td align="left">479</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.04256</td>
<td align="char" char=".">2.51956</td>
<td align="char" char=".">&#x2212;3.55</td>
<td align="char" char=".">1.6225</td>
<td align="center">miR-1299</td>
</tr>
<tr>
<td align="left">284</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.04671</td>
<td align="char" char=".">2.45336</td>
<td align="char" char=".">&#x2212;3.63</td>
<td align="char" char=".">2.745</td>
<td align="center">miR-218-5p</td>
</tr>
<tr>
<td align="left">731</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.04784</td>
<td align="char" char=".">2.43638</td>
<td align="char" char=".">&#x2212;3.65</td>
<td align="char" char=".">1.1275</td>
<td align="center">miR-3116</td>
</tr>
<tr>
<td align="left">636</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.04941</td>
<td align="char" char=".">2.41358</td>
<td align="char" char=".">&#x2212;3.68</td>
<td align="char" char=".">1.4275</td>
<td align="center">miR-499a-5p</td>
</tr>
<tr>
<td align="left">1</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.0471</td>
<td align="char" char=".">&#x2212;2.44742</td>
<td align="char" char=".">&#x2212;3.64</td>
<td align="char" char=".">&#x2212;1.02</td>
<td align="center">miR-346</td>
</tr>
<tr>
<td align="left">813</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.04084</td>
<td align="char" char=".">&#x2212;2.54889</td>
<td align="char" char=".">&#x2212;3.51</td>
<td align="char" char=".">&#x2212;1.475</td>
<td align="center">miR-1193</td>
</tr>
<tr>
<td align="left">1,067</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.03396</td>
<td align="char" char=".">&#x2212;2.68116</td>
<td align="char" char=".">&#x2212;3.35</td>
<td align="char" char=".">&#x2212;1.045</td>
<td align="center">miR-4263</td>
</tr>
<tr>
<td align="left">264</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.028</td>
<td align="char" char=".">&#x2212;2.82107</td>
<td align="char" char=".">&#x2212;3.19</td>
<td align="char" char=".">&#x2212;1.0725</td>
<td align="center">miR-496</td>
</tr>
<tr>
<td align="left">889</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.02301</td>
<td align="char" char=".">&#x2212;2.96491</td>
<td align="char" char=".">&#x2212;3.02</td>
<td align="char" char=".">&#x2212;1.8875</td>
<td align="center">miR-2276-3p</td>
</tr>
<tr>
<td align="left">560</td>
<td align="char" char=".">0.773</td>
<td align="char" char=".">0.01371</td>
<td align="char" char=".">&#x2212;3.35482</td>
<td align="char" char=".">&#x2212;2.58</td>
<td align="char" char=".">&#x2212;1.2775</td>
<td align="center">miR-653-5p</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>DEG, differentially expressed mRNAs; DEM, differentially expressed miRNAs; DEL, differentially expressed lncRNAs; miR, miRNA; FC, fold change.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Identification of key differentially expressed miRNAs (DEMs) and construction of DEM-DEG Pairs <bold>(A)</bold> Venn diagram of key differentially expressed miRNAs between predicted miRNAs and DEMs. <bold>(B)</bold> The kDEM&#x2013;DEG pairs constructed by Cytoscape.</p>
</caption>
<graphic xlink:href="fgene-13-855557-g005.tif"/>
</fig>
</sec>
<sec id="s3-8">
<title>Identification of DELs and Establishment of the ceRNA Network</title>
<p>A total of 49 DELs were identified, of which 22 were up-regulated and 27 were down-regulated in GSE128813 (<xref ref-type="fig" rid="F6">Figure&#x20;6A</xref>). The interactive miRNAs of DELs were predicted by mircode. Among these 49 lncRNAs, ARAP1-AS2, and AC104781 interacted with the five key miRNAs. ceRNA network was constructed by Cytoscape (<xref ref-type="fig" rid="F6">Figure&#x20;6B</xref>). There are two networks containing hub gene: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Identification of differentially expressed lncRNAs (DELs) and Establishment of the ceRNA network <bold>(A)</bold> Heatmap of the DELs (red: high expression; blue: low expression) <bold>(B)</bold> The ceRNA network in Rheumatoid arthritis (RA) (ellipse: mRNA, triangle: miRNA, and hexagonal: lncRNA).</p>
</caption>
<graphic xlink:href="fgene-13-855557-g006.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>RA is one of the most common chronic autoimmune disease and is heterogeneous with complex pathogeneses. Although there is an accumulating evidence suggesting the involvement of novel transcripts in the development of RA, detailed mechanisms still remain elusive. In this study, we obtained the 418 DEGs in FLS from RA patients and OA patients. We further perform a tissue-specific expression analysis of DEGs, the result reveals that the hematologic/immune system is the greatest distribution system, which may explain the disease occurrence of&#x20;RA.</p>
<p>Module analysis of the PPI network using ClueGO uncovered that NF-&#x3ba;B pathway may perform a significant role in RA pathogenesis. This has been confirmed by a number of studies. B&#x20;cell activating factor of TNF family (BAFF) promoted synovial inflammation by activating of B&#x20;cells in RA through NF-&#x3ba;B signaling pathway (<xref ref-type="bibr" rid="B48">Zhang et&#x20;al., 2021</xref>). Growth differentiation factor 11 (GDF11) was able to significantly restrain the nuclear factor kappa-light-chain-enhancer of activated B&#x20;cells (NF-&#x3ba;B) signaling pathway and prevent the development of RA (<xref ref-type="bibr" rid="B24">Li et&#x20;al., 2019</xref>). Overexpression of miR-496 in RA-FLS may inhibit cell proliferation by inactivating the NF-&#x3ba;B pathway (<xref ref-type="bibr" rid="B46">Xing et&#x20;al., 2021</xref>). CS-semi5, a semisynthetic chondroitin sulfate, may effectively improve synovial inflammation, and cartilage erosion in RA through NF-&#x3ba;B deactivation (<xref ref-type="bibr" rid="B26">Li et&#x20;al., 2021</xref>). Hence, the intrinsic relationship between NF-&#x3ba;B pathway and RA needs to be further studied in the future.</p>
<p>Module analysis of the PPI network using MCODE identified 3&#x20;tissue-specific expressed hub genes involving in the hematologic/immune system. SPAG5 is a Protein Coding gene involved in the functional and dynamic regulation of mitotic spindles. There is growing evidence that it is upregulated in many human cancers, acting as an oncogene and promoting cell proliferation (<xref ref-type="bibr" rid="B29">Liu et&#x20;al., 2018</xref>; <xref ref-type="bibr" rid="B16">He et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B3">Canu et&#x20;al., 2021</xref>). CUX2 is a Protein Coding gene encoding a transcription factor which contains three CUT domains and a homeodomain. CUX2 functions as an accessory factor in the repair of oxidative DNA damage (<xref ref-type="bibr" rid="B34">Pal et&#x20;al., 2015</xref>) and plays an important role in the neuron specification and spine development (<xref ref-type="bibr" rid="B11">Cubelos et&#x20;al., 2010</xref>). THEMIS2, also known as ICB1 (Induced on contact with basement membrane 1), is a protein expressed in B&#x20;cells and is associated with signaling proteins Grb2 and Vav1 (<xref ref-type="bibr" rid="B15">Hartweger et&#x20;al., 2014</xref>). Themis2 is not required for B&#x20;cell development and activation, but it lowers the threshold for activation of B&#x20;cells in positive selection (<xref ref-type="bibr" rid="B7">Cheng et&#x20;al., 2017</xref>). Themis2 may constitute a control point in macrophage inflammatory response (<xref ref-type="bibr" rid="B35">Peirce et&#x20;al., 2010</xref>). There have been no reports of three tissue-specific expressed hub genes in RA-related research. Nonetheless, SPAG5 and THEMIS2 were overexpressed in the synovial samples of RA in our study. In addition, the results of ROC analysis revealed that these genes had an important diagnostic value for RA. Therefore, these findings suggest that SPAG5 and THEMIS2 may play an essential in progression of synovial hyperplasia.</p>
<p>After interacting 147&#x20;DEGs-related target miRNAs and 23 DEMs, 5 overlapping miRNAs were obtained as key miRNAs for further study. In addition to miR-20b and miR-30c, the roles of the other three miRNAs in RA have been reported. miR-496 can impair the proliferative ability and facilitate the apoptosis of IL-1&#x3b2;-treated MH7A through regulating MMP10 expression and NF-&#x3ba;B signaling pathway (<xref ref-type="bibr" rid="B46">Xing et&#x20;al., 2021</xref>). miR-218-5p knockdown could modulate the proliferation, apoptosis and autophagy of RASFs by upregulating KLF9, and suppressing the activation of the JAK2/STAT3 signaling pathway (<xref ref-type="bibr" rid="B6">Chen et&#x20;al., 2021</xref>). MiR-218 regulates the osteogenic differentiation of RA-FLS via the ROBO1/DKK-1 axis (<xref ref-type="bibr" rid="B18">Iwamoto et&#x20;al., 2018</xref>). miR-26a could enhances cells proliferation and attenuate apoptosis of chondrocytes in RA rats (<xref ref-type="bibr" rid="B20">Jiang and Cao, 2020</xref>). In addition, miR-26a ameliorates the arthritis severity of the rats through directly targeting TLR3 in rat macrophages (<xref ref-type="bibr" rid="B19">Jiang et&#x20;al., 2014</xref>). Given its prominent role in RA, plasma miR-26a has been proposed as the most promising non-invasive biomarkers for disease detection (<xref ref-type="bibr" rid="B30">Liu et&#x20;al., 2019</xref>). Although there is no relevant research on miR-20b and miR-30c in RA, studies have shown that both of them have a definite effect on inflammation. miR-30c-5p adjusts macrophage-mediated inflammation through pro-apoptotic signals (<xref ref-type="bibr" rid="B5">Ceolotto et&#x20;al., 2017</xref>). knockdown of lncRNA HOTAIR inhibits inflammatory cytokine secretion by increasing the expression of miR-20b and decreasing the expression of NLRP3, thereby relieving ankle swelling caused by gouty arthritis (<xref ref-type="bibr" rid="B31">Liu et&#x20;al., 2021</xref>). miR-20b can restraint T&#x20;Cell proliferation and activation by regulating NFAT Signaling Pathway in Thymoma-Associated Myasthenia Gravis (<xref ref-type="bibr" rid="B44">Xin et&#x20;al., 2016</xref>). However, given that these results are based only on bioinformatics models and literature reports, further research is essential to validate the in-depth role of these 5 key miRNAs in&#x20;RA.</p>
<p>We establish a ceRNA regulatory network was constructed based on five key miRNAs to investigate the role of lncRNA in RA. There are two regulatory axes involving hub genes among the network, including ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB. Interestingly, the key miRNAs involved in these two regulatory networks are the same ones that have not been reported for RA. TRIM3 and FRZB are hub genes involved in the regulation of these two ceRNA axes, both are down regulated in our study. It is worth noting that our finding of FRZB differ from a prior study on circulating levels of FRZB in patients with early rheumatoid arthritis (<xref ref-type="bibr" rid="B10">Corallini et&#x20;al., 2010</xref>). Experimental study has shown that TRIM3 expression in synovial tissue samples from patients with RA was lower than that of healthy controls, which is consistent with our findings, may play an anti-proliferative role in RA-FLS through the P38 signaling pathway (<xref ref-type="bibr" rid="B42">Wang et&#x20;al., 2017</xref>). Our findings also offer insights into the roles of ARAP1-AS2. Unlike for rheumatoid arthritis, the study by Yang et&#x20;al. showed that lncRNA-ARAP1-AS2 gradually up-regulated with the progression of diabetes (<xref ref-type="bibr" rid="B47">Yang et&#x20;al., 2019</xref>). Mechanistically, lncRNA ARAP1-AS2 may participate in high glucose-induced proximal tubular cell injury by interacting with ARAP1 through persistent activation of EGFR/TGF-&#x3b2;/Smad3 pathway (<xref ref-type="bibr" rid="B25">Li X et&#x20;al., 2020</xref>). ARAP1-AS2/ARAP1 may affect EMT processes and cytoskeleton rearrangement in human renal tubular epithelial cells via boosting of Cdc42-GTP levels (<xref ref-type="bibr" rid="B23">Li L et&#x20;al., 2020</xref>). Although lncRNA ARAP1-AS2 has not been reported in RA, we assumed that ARAP1-AS2/miR-20b-3p/TRIM3 or ARAP1-AS2/miR-30c-3p/FRZB might be the key regulatory axes in the pathogenesis of RA based on the above analysis.</p>
<p>According to CMap analysis, five potential compounds for RA treatment were obtained, and their targets, and application scope were further searched. Troleandomycin (TAO) is one of cytochrome P-450 (CYP450) monooxygenases blockers, which is considered to be a novel drug in the treatment of human inflammatory bowel disease (<xref ref-type="bibr" rid="B38">Sasaki et&#x20;al., 2003</xref>). This suggests that its therapeutic role in RA is also worth exploring. Antirheumatic agents are effective for Parkinson&#x2019;s symptoms, but not for levodopa (<xref ref-type="bibr" rid="B27">Lee et&#x20;al., 2020</xref>). Nonselective HDAC inhibitor trichostatin A (TSA) shared multiple molecular mechanism in suppressing inflammation and may represent a new principle in the treatment of RA (<xref ref-type="bibr" rid="B9">Chung et&#x20;al., 2003</xref>; <xref ref-type="bibr" rid="B13">Gillespie et&#x20;al., 2012</xref>; <xref ref-type="bibr" rid="B14">Grabiec et&#x20;al., 2012</xref>). As a PI3K/AKT inhibitor, LY294002 is often used in RA-related studies (<xref ref-type="bibr" rid="B2">Brennan et&#x20;al., 2002</xref>; <xref ref-type="bibr" rid="B21">Kim et&#x20;al., 2005</xref>; <xref ref-type="bibr" rid="B45">Xing et&#x20;al., 2016</xref>). This presents a number of possibilities for therapeutic strategies. Levamisole was previously used to treat rheumatoid arthritis due to its immunomodulatory properties. However, it had been withdrawn from the US market because of serious side-effects (<xref ref-type="bibr" rid="B4">Cascio and Jen, 2018</xref>).</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>Conclusion</title>
<p>This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for&#x20;RA.</p>
</sec>
</body>
<back>
<sec id="s6">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>XX and YZ conceived and designed the study. QX downloaded and analyzed the data. BG was responsible for the visualization of data. XX wrote the original draft of the manuscript. ZS and YZ supervised this work and revised the manuscript. All authors have read and approved the final manuscript.</p>
</sec>
<sec id="s8">
<title>Funding</title>
<p>The study was supported by Tianjin Applied basic Research project (No. 21JCYBJC00130), Science and Technology talent cultivation Project of Tianjin Health Commission (No. RC20156), and Science and Technology Fund of Tianjin First Central Hospital (No. 2020CM10).</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<ack>
<p>Thanks to the researchers who uploaded the data. We would like to thank the academic editor and reviewers for their important contributions that improved the quality of this article.</p>
</ack>
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