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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2022.850993</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Integrated Bioinformatics and Validation Reveal IL1B and Its Related Molecules as Potential Biomarkers in Chronic Spontaneous Urticaria</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Peng</surname>
<given-names>Shixiong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1609708"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Teng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Sisi</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Tang</surname>
<given-names>Qian</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yan</surname>
<given-names>Yang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Feng</surname>
<given-names>Hao</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1205334"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Department of Dermatology, The First Affiliated Hospital of Hunan Normal University/Hunan Provincial People&#x2019;s Hospital</institution>, <addr-line>Changsha</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Dermatology, Chinese Traditional Hospital of Changsha</institution>, <addr-line>Changsha</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Nursing Department, Hunan Provincial People&#x2019;s Hospital/The First Affiliated Hospital of Hunan Normal University</institution>, <addr-line>Changsha</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Richard Williams, University of Oxford, United Kingdom</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Basavaraj Mallikarjunayya Vastrad, KLE Society&#x2019;s College of Pharmacy, India; Ilaria Puxeddu, University of Pisa, Italy</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Hao Feng, <email xlink:href="mailto:doctorfenghao@126.com">doctorfenghao@126.com</email> </p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Autoimmune and Autoinflammatory Disorders, a section of the journal Frontiers in Immunology</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>850993</elocation-id>
<history>
<date date-type="received">
<day>08</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>22</day>
<month>02</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Peng, Zhang, Zhang, Tang, Yan and Feng</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Peng, Zhang, Zhang, Tang, Yan and Feng</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 terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>The etiopathogenesis of chronic spontaneous urticaria (CSU) has not been fully understood, and there has been extensive interest in the interaction between inflammatory dermatosis and pyroptosis. This study intends to investigate the molecular mechanism of pyroptosis-related genes in CSU <italic>via</italic> bioinformatic ways, aiming at identifying the potential key biomarker.</p>
</sec>
<sec>
<title>Methods</title>
<p>GSE72540, the RNA expression profile dataset of CSU, was utilized as the training set, and GSE57178 as the validation set. Differently expressed pyroptosis-related genes (DEPRGs), GO, KEGG, and DO analyses were performed. The hub genes were explored by the protein&#x2013;protein interaction analysis. Moreover, CIBERSORT was employed for estimating immune cell types and proportions. Then, we constructed a DEmRNA&#x2013;miRNA&#x2013;DElncRNA ceRNA network and a drug&#x2013;gene interaction network. Finally, ELISA was used for gene expression analysis.</p>
</sec>
<sec>
<title>Results</title>
<p>We recognized 17 DEPRGs, whose enrichment analyses showed that they were mostly enriched in inflammatory response and immunomodulation. Moreover, 5 hub genes (IL1B, TNF, and IRF1 are upregulated, HMGB1 and P2RX7 are downregulated) were identified <italic>via</italic> the PPI network and verified by a validation set. Then immune infiltration analysis displayed that compared with normal tissue, CSU owned a significantly higher proportion of mast cells activated, but a lower proportion of T cells CD4 naive and so on. Furthermore, IL1B was statistically and positively associated with mast cells activated in CSU, and SNHG3, the upstream factor of IL1B in the ceRNA we constructed, also related with mast cells in CSU. Further analysis exhibited that the protein subcellular localization of IL1B was extracellular, according with its intercellular regulation role; IL1B was significantly correlated with key immune checkpoints; and the NOD-like receptor signaling pathway was the mainly involved pathway of IL1B based on the couple databases. What is more, the result of ELISA of CSU patients was the same as the above analyses about IL1B. In&#xa0;addition, the drug&#x2013;gene interaction network contained 15 potential therapeutic drugs targeting IL1B, and molecular docking might make this relationship viable.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>IL1B and its related molecules might play a key role in the development of CSU and could be potential biomarkers in CSU.</p>
</sec>
</abstract>
<kwd-group>
<kwd>chronic spontaneous urticaria (CSU)</kwd>
<kwd>pyroptosis-related genes</kwd>
<kwd>IL1B, bioinformatics</kwd>
<kwd>inflammation</kwd>
<kwd>immunology</kwd>
</kwd-group>
<counts>
<fig-count count="11"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="55"/>
<page-count count="16"/>
<word-count count="5121"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Chronic spontaneous urticaria (CSU) is one of chronic inflammatory dermatosis, which is delineated as, for recognized or unrecognized causes, angioedema, wheal, or both occurring spontaneously for more than 6 weeks (<xref ref-type="bibr" rid="B1">1</xref>). The CSU&#x2019;s prevalence is approximately 1% of the population (lifetime prevalence = 1.4%; point prevalence = 0.7%) (<xref ref-type="bibr" rid="B2">2</xref>). Moreover, CSU will get the increase of risk for comorbid autoimmune diseases like autoimmune thyroid disease (<xref ref-type="bibr" rid="B3">3</xref>). The frequently recurring symptoms, pruritus, urticaria, and angioedema, severely affect patients&#x2019; performance at school and work and impair their quality of life, which brings much encumbrance to both their households and society (<xref ref-type="bibr" rid="B4">4</xref>). Unlike acute urticaria, which is usually caused by an identifiable agent like an allergic reaction to a drug or other, the cause and pathogenesis of CSU are complex and remain largely unclear (<xref ref-type="bibr" rid="B5">5</xref>). Consequently, it is of great significance for individualized and effective treatment to reveal the pathogenesis and recognize key biomarkers of CSU.</p>
<p>The CSU&#x2019;s etiopathogenesis has not been totally uncovered, but the existing studies suggest that maladjustment of inflammatory cells (such as mast cells and basophils) is the potentially core contributor (<xref ref-type="bibr" rid="B6">6</xref>). It is well known that a series of intracellular signaling cascades result in mast cell activation, after IgE bonds to the high-affinity IgE receptor. The activated mast cell releases proteases, histamines, and cytokines with the generation of platelet-activating mediators and other arachidonic acid metabolites (leukotrienes C4, D4, and E4 and prostaglandin D2). These cytokines give rise to vascular permeability and increased vasodilation, ensuing interstitial edema and sensory nerve stimulation and causing the obvious itchiness, redness, and swelling (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B8">8</xref>). Besides, some CSU patients could show signs of activation of the coagulation/fibrinolytic system, such as significant elevation of serum factors like D-dimer, sICAM-1, and sVCAM-1 (<xref ref-type="bibr" rid="B9">9</xref>, <xref ref-type="bibr" rid="B10">10</xref>). In this setting, some treatment strategies were developed, such as antihistamine, biological agent, and immunosuppressant. The first-line symptomatic treatment for CSU is largely depending on modern 2nd-generation H1 antihistamines, but standard-dosed antihistamines are ineffective in about 40% of the patients (<xref ref-type="bibr" rid="B11">11</xref>). Omalizumab, a humanized anti-IgE antibody, is the first licensed biological treatment by the Food and Drug Administration (FDA) for patients with CSU refractory to H1 antihistamines. However, relapse rates following the withdrawal of omalizumab are high (<xref ref-type="bibr" rid="B12">12</xref>). Ciclosporin is recommended for combination treatment in patients with severe disease refractory. Nevertheless, it is a problem which cannot be ignored that long-term use of ciclosporin leads to serious side effects (<xref ref-type="bibr" rid="B13">13</xref>). It can thus be seen that complete control of symptoms in the majority of patients remains a worldwide challenge. Consequently, further and fuller exploring the inflammatory reaction pathogenesis of CSU is scientifically significant to the clinical therapy.</p>
<p>There has been extensive interest in the interaction between inflammatory reaction and pyroptosis. The pyroptosis is denoted as inflammasome-dependent cell death (<xref ref-type="bibr" rid="B14">14</xref>). It was found that it makes a critical difference in the development of numerous inflammatory skin diseases. A research reported that Mdivi-1 significantly suppressed the pyroptotic cell death of keratinocytes and inhibited NLRP3 inflammasome activation to play a protective role in atopic dermatitis (<xref ref-type="bibr" rid="B15">15</xref>). Deng et&#xa0;al. indicated that cycloastragenol could inhibit the liberation of inflammatory mediators and macrophage infiltration in psoriasis by inhibiting the pyroptosis which NLRP3 mediated (<xref ref-type="bibr" rid="B16">16</xref>). In addition, previous research has established that aberrant NLRP3 inflammasome activation in masts cell contributes to histamine-independent urticaria by production of IL-1&#x3b2; in cryopyrin-associated periodic syndromes (CAPSs) (<xref ref-type="bibr" rid="B17">17</xref>). However, our understanding of CSU with pyroptosis is still pretty limited.</p>
<p>Microarray technology has been widely utilized into biological studies in recent years, and the data generated by it like the mRNA dataset could be an advantageous instrument for discovering critical factors of etiopathogenesis of diseases, which offers valuable insulation and foundation for further novel studies (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>). In this study, <italic>via</italic> applying the bioinformatic method, we analyzed the data of the Gene Expression Omnibus (GEO) (<xref ref-type="bibr" rid="B20">20</xref>), which has its origin in microarray technology, to explore immune cell infiltration and ceRNA network, and further reveal the molecular mechanism of pyroptosis-related genes in CSU and identify key biomarkers.</p>
</sec>
<sec id="s2">
<title>Material and Method</title>
<sec id="s2_1">
<title>Microarray Data Source</title>
<p>The analysis process of this study is shown in <xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>. We downloaded the datasets (GSE72540, GSE57178) from the GEO database (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). GSE72540 contained 31 samples&#x2019; RNA expression profiling, selecting 10 CSU samples and 8 control samples, and GSE57178 contained 18 samples, selecting 6 CSU samples and 7 control samples.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Flowchart of the study.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Details of the GEO CSU data.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Dataset</th>
<th valign="top" align="center">Platform</th>
<th valign="top" align="center">Number of samples (CSU/control, subjects)</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">GSE72540</td>
<td valign="top" align="left">GPL16699</td>
<td valign="top" align="center">31 (10/8 18)</td>
</tr>
<tr>
<td valign="top" rowspan="3" align="left">GSE57178</td>
<td valign="top" align="left">Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Feature Number version)</td>
<td valign="top" rowspan="3" align="center">18 (6/7 13)</td>
</tr>
<tr>
<td valign="top" align="left">GPL6244</td>
</tr>
<tr>
<td valign="top" align="left">[HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>GEO, Gene Expression Omnibus; CSU, chronic spontaneous urticaria.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2_2">
<title>Identifying Differently Expressed Pyroptosis-Related Genes</title>
<p>We normalized and preprocessed data and identified the different expression genes (DEGs) among the CSU sample and control <italic>via</italic> the GEO2R tool (<xref ref-type="bibr" rid="B21">21</xref>). |log<sub>2</sub> FC| &gt;1 and p &lt; 0.05 as the cutoff. The 161 pyroptosis-related genes (PRGs) were downloaded from the GeneCards database (<xref ref-type="supplementary-material" rid="ST1">
<bold>Supplementary Table S1</bold>
</xref>) (<xref ref-type="bibr" rid="B22">22</xref>). Altogether consistent genes between DEGs and PRGs were identified as differently expressed pyroptosis-related genes (DEPRGs).</p>
</sec>
<sec id="s2_3">
<title>GO, KEGG, and DO Enrichment Analyses of DEPRGs</title>
<p>GO enrichment analysis [included MF (molecular function), BP (biological process), and CC (cellular component)] and KEGG pathway analysis were executed <italic>via</italic> the Metascape database (<xref ref-type="bibr" rid="B23">23</xref>). Min Enrichment &#x2265;1.5, Min Overlap &#x2265;3, and p &lt; 0.01 were considered as the threshold. The WebGestalt tool (<xref ref-type="bibr" rid="B24">24</xref>) was used for DO enrichment analysis, and FDR &#x2264; 0.05 as the significance level.</p>
</sec>
<sec id="s2_4">
<title>Protein&#x2013;Protein Interaction Network and Module Analyses</title>
<p>To investigate the protein&#x2013;protein interaction (PPI) network, we used the STRING tool (<xref ref-type="bibr" rid="B25">25</xref>) and visualized it and analyzed the interactions of DEPRGs by the Cytoscape software (<xref ref-type="bibr" rid="B26">26</xref>). The&#xa0;Molecular Complex Detection (MCODE) plug-in was utilized for the module analysis of the PPI network. The cytoHubba tool was used for identifying the hub genes. The hub genes&#x2019; GO enrichment analysis was performed through the ClueGO plug-in.</p>
</sec>
<sec id="s2_5">
<title>Data Verification</title>
<p>The RNA expressed dataset GSE57178, containing 6 CSU lesion samples and 7 healthy control samples, was utilized as the validation set to verify the reliability of hub genes.</p>
</sec>
<sec id="s2_6">
<title>Immune Infiltration Analysis</title>
<p>The immune infiltration was calculated by the web tool CIBERSORT (<xref ref-type="bibr" rid="B27">27</xref>), which is a deconvolution algorithm that can evaluate the proportion of 22 infiltrating lymphocyte subsets in a large number of tissue samples. The GraphPad Prism 8.0.2 (San Diego, CA, USA) tool (<xref ref-type="bibr" rid="B28">28</xref>) was utilized for the correlation analysis between different immune cells, and between immune cells and hub genes, calculating the ratio of every kind of immune cell in CSU tissue and control.</p>
</sec>
<sec id="s2_7">
<title>Exploration ceRNA Network of the Hub Genes</title>
<p>To explore the miRNA&#x2013;mRNA interaction of the ceRNA network, the potential miRNAs targeting the hub gene were identified <italic>via</italic> the TargetScan (<xref ref-type="bibr" rid="B29">29</xref>), miRNet (<xref ref-type="bibr" rid="B30">30</xref>), and DIANA TOOLS TarBase v.8 databases (<xref ref-type="bibr" rid="B31">31</xref>). If this was concurrently recognized in each database, the result was considered as true. Next, the possible lncRNAs targeting the miRNA were predicted through the miRNet database, which was cross-checked with the differently expressed lncRNA (DElncRNA) of CSU. LncRNA subcellular localization was predicted using lncLocator (<xref ref-type="bibr" rid="B32">32</xref>). The web-based tools, Wei Sheng Xin (<uri xlink:href="http://www.bioinformatics.com.cn">http://www.bioinformatics.com.cn</uri>) and Draw Venn Diagram (<uri xlink:href="http://bioinformatics.psb.ugent.be/webtools/Venn/">http://bioinformatics.psb.ugent.be/webtools/Venn/</uri>), were used for data visualization.</p>
</sec>
<sec id="s2_8">
<title>Gene Set Enrichment Analysis</title>
<p>The Gene Set Enrichment Analysis (GSEA) tool (<xref ref-type="bibr" rid="B33">33</xref>) was used for exploring the molecular signaling pathway in which IL1B might be involved in CSU. The pathway enrichment analysis utilized the c2.cp.kegg.v7.3.symbols.gmt gene sets of the official website. False discovery rate q-value &lt;0.01 was regarded as difference.</p>
</sec>
<sec id="s2_9">
<title>Analysis of Protein Subcellular Localization and Correlation With Immune Checkpoints</title>
<p>The protein subcellular localization of IL1B was predicted using the Cell-PLoc 2.0 tool (<xref ref-type="bibr" rid="B34">34</xref>), which is a package of web servers for predicting subcellular localization of proteins in different organisms. The correlation between IL1B and key immune checkpoints (<xref ref-type="bibr" rid="B35">35</xref>) such as HAVCR2(TIM3), LAG3, CTLA4, CD274(PDL1), PDCD1(PD1), and TIGIT were analyzed <italic>via</italic> Pearson&#x2019;s correlation coefficient in GraphPad Prism 8.0.2.</p>
</sec>
<sec id="s2_10">
<title>Drug&#x2013;Gene Interaction and Molecular Docking Analysis</title>
<p>To explore the drug&#x2013;gene interaction, the DrugBank database (<xref ref-type="bibr" rid="B36">36</xref>) was utilized for identifying existing or/and potentially associated drug substances. Moreover, the Cytoscape software was utilized for data visualization. The molecular structure of the ligand and the target protein were obtained from the PubChem database (<xref ref-type="bibr" rid="B37">37</xref>) and PDB database (<xref ref-type="bibr" rid="B38">38</xref>). Docking simulations were conducted through AutoDock Vina (<xref ref-type="bibr" rid="B39">39</xref>) to generate the docking energy. The PyMOL software (<xref ref-type="bibr" rid="B40">40</xref>) was performed to visualize docked complexes.</p>
</sec>
<sec id="s2_11">
<title>Enzyme-Linked Immunosorbent Assay</title>
<p>To examine the protein levels of IL1B, NLRP3, and mast cell tryptase (MCT), serums from 10 CSU patients and 10 healthy controls were harvested for enzyme-linked immunosorbent assay (ELISA) (the CSU patients without medical treatment within 2 weeks and concomitant autoimmune diseases, whose detailed information is in <xref ref-type="supplementary-material" rid="ST2">
<bold>Supplementary Table S2</bold>
</xref>). Specific ELISA kits for IL1B (Neobioscience, Shenzhen, China), NLRP3 (uscnk, Wuhan, China), and MCT (Fufeng, Shanghai, China) were used according to the instructions of the manufacturer. Briefly, the standard samples, which were offered through the kit, of known concentration and the samples from two experimental groups were added to the kit plate and then incubated with the kit reagents (<xref ref-type="bibr" rid="B41">41</xref>). The OD450 values were detected using the microplate reader (Huisong, Shenzhen, China).</p>
</sec>
<sec id="s2_12">
<title>Statistical Analysis</title>
<p>The unpaired Student&#x2019;s t-test was performed for data analysis of two groups. The potential correlation between the two variables was detected by Pearson&#x2019;s correlation coefficient. p &lt; 0.05 was considered as significance level. GraphPad Prism 8.0.2 was performed as the statistical software.</p>
</sec>
</sec>
<sec id="s3">
<title>Results</title>
<sec id="s3_1">
<title>Recognition of DEPRGs of CSU</title>
<p>The CSU RNA expression profile dataset (GSE72540) was normalized as shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2A</bold>
</xref>. 1,297 DEGs (containing 1,033 DEmRNAs and 173 DElncRNAs) were identified in the GSE72540 dataset (<xref ref-type="supplementary-material" rid="ST3">
<bold>Supplementary Table S3</bold>
</xref>), and their volcano plot is shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2B</bold>
</xref>. As shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2C</bold>
</xref> and <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, we identified 17 congruous DEPRGs <italic>via</italic> integrated bioinformatics analysis, including 13 congruously upregulated and 4 congruously downregulated. The heat map of DEPRGs is shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2D</bold>
</xref>.</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Identification of DEPRGs of CSU. <bold>(A)</bold> Normalization of selected samples of GSE72540. <bold>(B)</bold> The differentially expressed genes of GSE72540. <bold>(C)</bold> The DEPRGs of CSU. <bold>(D)</bold> The heat map of the DEPRGs.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g002.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>The DEPRGs of CSU.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Regulation</th>
<th valign="top" align="center">DEPRGs</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Upregulated (n = 13)</td>
<td valign="top" align="left">SERPINB1 GBP1 IFI16 GSDMA GZMB BIRC3 IL1B IRF1 CD14 PRDM1 IL36G PANX1 TNF</td>
</tr>
<tr>
<td valign="top" align="left">Downregulated (n = 4)</td>
<td valign="top" align="left">PECAM1 P2RX7 MST1 HMGB1</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>DEPRGs, differently expressed pyroptosis-related genes; CSU, chronic spontaneous urticaria.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Function Enrichment Analyses of the DEPRGs</title>
<p>The GO analysis of DEPRGs was performed to reveal their biology functions. As shown, in the GO BP category, most of DEPRGs were mostly involved into regulation of cytokine production, interleukin-1 beta production, interleukin-1 production, etc. (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). In the GO CC category, most of the DEPRGs were enriched into the membrane microdomain and membrane raft (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>). In the GO MF category, the main DEPRGs were enriched in cytokine activities, cytokine receptor binding, and signaling receptor activator activity, etc. (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3C</bold>
</xref>). The results of KEGG pathway enrichment exhibited that the mostly involved pathways were the NF-kappa B signaling pathway, TNF signaling pathway, and NOD-like receptor signaling pathway (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3D</bold>
</xref>). Utilizing the WebGestalt online database to further explore the function of DEPRGs, the result of DO enrichment showed that dermatomyositis, leishmaniasis, cutaneous, ulcerative colitis, etc., were the major diseases that DEPRGs participated in (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3E</bold>
</xref>). These suggested that inflammation and immune response were the major function of DEPRGs.</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>The top ten lists of function enrichment analyses of DEPRGs. <bold>(A)</bold> GO BP; <bold>(B)</bold> GO CC; <bold>(C)</bold> GO MF; <bold>(D)</bold> KEGG signaling pathway; <bold>(E)</bold> DO enrichment. **p &lt; 0.01; ***p &lt; 0.001.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g003.tif"/>
</fig>
</sec>
<sec id="s3_3">
<title>PPI Network and Hub Gene Analyses</title>
<p>To reveal the interaction of each protein, the PPI network of the DEPRGs was built according to the STRING database, including 15 nodes and 34 edges. In the protein network graph, each node represented a protein, and the edge represented a connection between two proteins. Moreover, among the 15 nodes, 3 nodes were downregulated, and 12 were upregulated (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>). The targets were sorted by target connectivity from small to large in the PPI network, as shown in <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>. The most important module was selected, including 10 edges and 8 nodes (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4C</bold>
</xref>). Hub genes were detected consistently <italic>via</italic> four algorithms (degree, MNC, stress, and MCC) of cytoHubba (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4D</bold>
</xref>). The top five gene scores were considered to be hub genes of CSU: IL1B, TNF, IRF1, HMGB1, and P2RX7 (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4E</bold>
</xref> and <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). Because the more closely knitted gene in the network is more fundamental to regulation, we further investigated the functions of the hub genes through the ClueGO plug-in. As <xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4F</bold>
</xref> shows, they were still primarily enriched in immunomodulation including regulation of adaptive immune response, lymphocyte proliferation, and regulation of phagocytosis. According to GSE57178, the mRNA expression of each hub gene manifested that, compared with the control, IL1B, IRF1, and TNF were significantly overexpressed while P2RX7 had a significantly lower expression in CSU, which was the same as the above results and indicated that IL1B, IRF1, P2RX7, and TNF were the key genes of CSU (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4G</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>The PPI network and hub gene analyses. <bold>(A)</bold> The PPI network of the DEPRGs, the bigger sizes of the edge and node mean the higher degree. The red means upregulated, and green means downregulated. <bold>(B)</bold> The connectivity rank of genes. <bold>(C)</bold> The first module of the PPI network. <bold>(D)</bold> Four algorithms were utilized to identified hub genes. <bold>(E)</bold> 5 hub genes of CSU. <bold>(F)</bold> The biological process of hub genes <italic>via</italic> the ClueGO tool. <bold>(G)</bold> Data validation of hub genes by GSE57178. *P &lt; 0.05; **P &lt; 0.01; ***P &lt; 0.001.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g004.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The top 5 hub genes.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Genes</th>
<th valign="top" align="center">Description</th>
<th valign="top" align="center">Degree</th>
<th valign="top" align="center">MCC</th>
<th valign="top" align="center">MNC</th>
<th valign="top" align="center">Sterss</th>
<th valign="top" align="center">Log<sub>2</sub>FC</th>
<th valign="top" align="center">Expression change</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">IL1B</td>
<td valign="top" align="left">Interleukin 1 beta</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">38</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">78</td>
<td valign="top" align="center">3.43222906</td>
<td valign="top" align="left">Upregulated</td>
</tr>
<tr>
<td valign="top" align="left">TNF</td>
<td valign="top" align="left">Tumor necrosis factor</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">130</td>
<td valign="top" align="center">1.26641463</td>
<td valign="top" align="left">Upregulated</td>
</tr>
<tr>
<td valign="top" align="left">IRF1</td>
<td valign="top" align="left">Interferon regulatory factor 1</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">26</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">48</td>
<td valign="top" align="center">1.17112479</td>
<td valign="top" align="left">Upregulated</td>
</tr>
<tr>
<td valign="top" align="left">HMGB1</td>
<td valign="top" align="left">High mobility group box 1</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">18</td>
<td valign="top" align="center">5</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">-1.01820995</td>
<td valign="top" align="left">Downregulated</td>
</tr>
<tr>
<td valign="top" align="left">P2RX7</td>
<td valign="top" align="left">Purinergic receptor P2X 7</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">4</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">-1.25950113</td>
<td valign="top" align="left">Downregulated</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>MCC, maximal clique centrality; MNC, maximum neighborhood component.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<title>Immune Infiltration Analysis</title>
<p>We investigated the difference among CSU tissues and control to explore the panorama of immune infiltration of CSU <italic>via</italic> the CIBERSORT algorithm. The ratio of 22 immune cells of samples is shown in <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5A</bold>
</xref>. The correlation between each of immune cells is shown in <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5B</bold>
</xref>, among which T cells CD4 memory activated were significantly correlated with macrophages M1 and macrophages M2, and eosinophils were statistically correlated with dendritic cells resting. At the side of control tissue, CSU owned a higher ratio of mast cells activated, and T cells CD4 naive, plasma cells, and B cells memory were significantly lower (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5C</bold>
</xref>). Next, we revealed the relation among the abundance of the immune cells and hub gene expression through the Pearson&#x2019;s correlation coefficient. The results displayed that mast cells activated were statistically positively related to the levels of IL1B and TNF, but negatively to HMGB1&#x2019;s; B cells memory and plasma cells were positively correlated with HMGB1 and P2RX7, but negatively with IL1B, IRF1, and TNF; T cells CD4 naive were positively correlated with HMGB1 and P2RX7, but negatively with IL1B (<xref ref-type="fig" rid="f6">
<bold>Figure&#xa0;6</bold>
</xref>).</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Immune infiltration analysis of CSU. <bold>(A)</bold> The ratio of 22 immune cells of each sample of CSU. <bold>(B)</bold> The correlation between each of immune cells. <bold>(C)</bold> The proportion of immune cells in CSU and control.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g005.tif"/>
</fig>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>The correlation between the hub gene and the immune cell. <bold>(A)</bold> HMGB1; <bold>(B)</bold> IL1B; <bold>(C)</bold> IRF1; <bold>(D)</bold> P2RX7; and <bold>(E)</bold> TNF.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g006.tif"/>
</fig>
</sec>
<sec id="s3_5">
<title>The mRNA&#x2013;miRNA&#x2013;lncRNA ceRNA Network of CSU</title>
<p>The non-coding RNA (ncRNA) never participates in encoding proteins but was discovered to be involved in many biological functions, and perturbation of mRNA&#x2013;miRNA&#x2013;lncRNA ceRNA networks may affect diseases. The miRNA-targeting hub gene was concurrently recognized by all object databases as true, and their Venn diagrams are shown in <xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7A</bold>
</xref>. In addition, the possible lncRNA targeting the miRNA was predicted <italic>via</italic> miRNet online databases and got the intersection with 173 DElncRNAs. The lncRNA and mRNA of ceRNA must have a consistent expression trend according to the ceRNA mechanism. Then, we got 9 unique DElncRNAs based on the above. The lncRNAs, which compete with miRNAs by acting as ceRNAs to regulate the expression of mRNA targets, should be in the cytoplasm. As a result, only 4 DElncRNAs (HOTAIR, SCARNA9, SNHG3, and TUG1) were predicted in the cytoplasm by lncLocator (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7B</bold>
</xref>). Finally, an 18-axis ceRNA network (containing HMGB1/hsa-mir-17-5p/HOTAIR, IL1B/hsa-mir-21-5p/SNHG3, P2RX7/hsa-mir-20a-5p/HOTAIR and so on) was identified (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7C</bold>
</xref>). In addition, HOTAIR, SCARNA9, SNHG3, and TUG1 were significantly related to the major infiltration cell of CSU (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref>).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>The construction of the lncRNA&#x2013;miRNA&#x2013;mRNA ceRNA network of CSU. <bold>(A)</bold> Venn diagram of miRNAs targeting each hub gene. <bold>(B)</bold> The subcellular localization of lncRNA of ceRNA. <bold>(C)</bold> The alluvial diagram of the ceRNA network.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g007.tif"/>
</fig>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>The correlation between the lncRNA of ceRNA and immune cells. <bold>(A)</bold> HOTAIR; <bold>(B)</bold> TUG1; <bold>(C)</bold> SNG3; <bold>(D)</bold> SCARNA9.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g008.tif"/>
</fig>
</sec>
<sec id="s3_6">
<title>GSEA of IL1B</title>
<p>Due to the fact that IL1B had been verified and that it played a role in immune infiltration and the ceRNA network of CSU, and simultaneously log<sub>2</sub>FC of IL1B was maximal in the hub genes, we chose IL1B for further analysis. The result of GSEA further verified the above results. As <xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9</bold>
</xref> shows, besides ubiquitin-mediated proteolysis, arachidonic acid metabolism, cytosolic DNA sensing pathway, galactose metabolism, and apoptosis, IL1B was still mainly enriched in the NOD-like receptor signaling pathway.</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>The GSEA of IL1B. <bold>(A)</bold> NOD-like receptor signaling pathway. <bold>(B)</bold> Apoptosis. <bold>(C)</bold> Arachidonic acid metabolism. <bold>(D)</bold> Cytosolic DNA sensing pathway. <bold>(E)</bold> Galactose metabolism. <bold>(F)</bold> Ubiquitin-mediated proteolysis.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g009.tif"/>
</fig>
</sec>
<sec id="s3_7">
<title>Protein Subcellular Localization and Correlation With Immune Checkpoint Analyses of IL1B</title>
<p>Different subcellular localizations of protein decide different&#xa0;biological functions. The protein subcellular localization of IL1B predicted by Cell-PLoc 2.0 was extracellular (<xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10A</bold>
</xref>). As displayed in <xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10B</bold>
</xref>, IL1B was significantly correlated with familiar immune checkpoints such as CD274(PDL1), CTLA4, HAVCR2(TIM3), and TIGIT, which indicated the important effect of IL1B in immune response further.</p>
<fig id="f10" position="float">
<label>Figure&#xa0;10</label>
<caption>
<p>Integrated analyses of IL1B. <bold>(A)</bold> protein subcellular localization of IL1B. <bold>(B)</bold> The correlation between immune checkpoints and IL1B. <bold>(C)</bold> Drug&#x2013;gene interaction network of IL1B. <bold>(D)</bold> Molecular docking between IL1B and minocycline.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g010.tif"/>
</fig>
</sec>
<sec id="s3_8">
<title>Drug&#x2013;Gene Interaction and Molecular Docking Analyses of IL1B</title>
<p>Developing potential therapeutic drugs for targeting IL1B provides a specific treatment strategy. The drug&#x2013;gene interaction network of IL1B is exhibited in <xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10C</bold>
</xref>, in which there were 15 potential therapeutic drugs identified and 7 of them were approved (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>). Then, we worked out the molecular binding site of IL1B and minocycline, one of approved small-molecule drugs (<xref ref-type="fig" rid="f10">
<bold>Figure&#xa0;10D</bold>
</xref>).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The drugs approved to interact IL1B.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">DrugBank ID</th>
<th valign="top" align="center">Name</th>
<th valign="top" align="center">Pharmacological action</th>
<th valign="top" align="center">Actions</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">DB01017</td>
<td valign="top" align="left">Minocycline</td>
<td valign="top" align="left">Unknown</td>
<td valign="top" align="left">Modulator</td>
</tr>
<tr>
<td valign="top" align="left">DB00843</td>
<td valign="top" align="left">Donepezil</td>
<td valign="top" align="left">Unknown</td>
<td valign="top" align="left">Inhibitor inducer</td>
</tr>
<tr>
<td valign="top" align="left">DB10772</td>
<td valign="top" align="left">Foreskin keratinocyte (neonatal)</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Agonist</td>
</tr>
<tr>
<td valign="top" align="left">DB06168</td>
<td valign="top" align="left">Canakinumab</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Binder</td>
</tr>
<tr>
<td valign="top" align="left">DB06372</td>
<td valign="top" align="left">Rilonacept</td>
<td valign="top" align="left">Unknown</td>
<td valign="top" align="left">Binder</td>
</tr>
<tr>
<td valign="top" align="left">DB05260</td>
<td valign="top" align="left">Gallium nitrate</td>
<td valign="top" align="left">Yes</td>
<td valign="top" align="left">Antagonist</td>
</tr>
<tr>
<td valign="top" align="left">DB11967</td>
<td valign="top" align="left">Binimetinib</td>
<td valign="top" align="left">Unknown</td>
<td valign="top" align="left">/</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3_9">
<title>IL1B Might Participate in Activation of Mast Cells <italic>via</italic> the NLRP3 in CSU</title>
<p>Compared with the healthy control, IL1B showed a significant overexpression in serum of CSU patients <italic>via</italic> ELISA, which is the same as our bioinformatic prediction (<xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11A</bold>
</xref>). The result of the ROC curve analysis showed that the area under the curve was 0.87 (p  0.01), which suggested the role of IL1B in diagnosis of CSU and further that it may be a potential biomarker in CSU (<xref ref-type="fig" rid="f11">
<bold>Figure&#xa0;11B</bold>
</xref>). Moreover, the result exhibited that MCT was overexpressed in CSU, and expression of MCT was statistically correlated with IL1B (<xref ref-type="fig" rid="f11">
<bold>Figures&#xa0;11C, D</bold>
</xref>
<bold>)</bold>. Due to the fact that MCT is the key marker of mast cell activation (<xref ref-type="bibr" rid="B42">42</xref>), it indicated that IL1B may participate in mast cell activation. NLRP3 is a subtype of NOD-like receptors and is famous for one of the key pyroptosis cytokines (<xref ref-type="bibr" rid="B43">43</xref>). The further ELISA result of CSU patient serum displayed that NLRP3 was significantly overexpressed and correlated with IL1B and MCT (<xref ref-type="fig" rid="f11">
<bold>Figures&#xa0;11E&#x2013;G</bold>
</xref>). It testified the above bioinformatic prediction again which IL1B participated in, in the NOD-like receptor signaling pathway. Moreover, it also advised that it could be <italic>via</italic> the NLRP3 that IL1B participates in activation of mast cells.</p>
<fig id="f11" position="float">
<label>Figure&#xa0;11</label>
<caption>
<p>IL1B might participate in activation of mast cells <italic>via</italic> the NLRP3 in CSU. <bold>(A)</bold> The expression of IL1B in CSU and control. <bold>(B)</bold> The ROC curve of IL1B. <bold>(C)</bold> The expression of MCT in CSU and control. <bold>(D)</bold> The correlation between IL1B and MCT in CSU. <bold>(E)</bold> The expression of NLRP3 in CSU and control. <bold>(F)</bold> The correlation between IL1B and NLRP3 in CSU. <bold>(G)</bold> The correlation between NLRP3 and MCT in CSU. **p &lt; 0.01, ***p &lt; 0.001, ****p &lt; 0.0001.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-13-850993-g011.tif"/>
</fig>
</sec>
</sec>
<sec id="s4">
<title>Discussion</title>
<p>CSU is a common chronic inflammatory dermatosis, which has significantly negative impacts on the quality of people&#x2019;s life owing to its repeated outbreaks and protracted course (<xref ref-type="bibr" rid="B44">44</xref>). Although current treatments of CSU get little effectiveness, how to more effectively mitigate and avert recurrence is still a global challenge as there are still many unknowns in its genesis. In addition, it has reached a consensus in the last guidelines that further research in some areas of CSU is needed, such as identification of mast cell/basophil-activating factors, identification of serum biomarkers of urticarial activity/mast cell activation, and identification of new histological markers (<xref ref-type="bibr" rid="B1">1</xref>). Remarkably, pyroptosis was one of deaths associated with cell membrane rupture. The increased number of cell membranes in mast cells might lead to the liberation of intracellular &#x3b2;-hexosaminidase and histamine (<xref ref-type="bibr" rid="B45">45</xref>). Since pyroptosis showed a great research prospect in inflammatory skin diseases, this work tries to identify and verify the potential key biomarkers of CSU from the standpoint of pyroptosis-related genes through bioinformatics ways, especially in inflammatory response, to provide a new perspective for the etiopathogenesis and therapeutic approaches of CSU.</p>
<p>In the present research, we recognized 1,297 DEGs from the CSU RNA expression profile. Then crossing the DEGS with pyroptosis-related genes, 17 DEPRGs (containing 4 downregulated genes and 13 upregulated genes) were recognized and then performed into gene function analysis. As shown, the DEPRGs were mostly involved in inflammatory response, as well as in pro-inflammatory effects (such as positive regulation of interleukin-8 production, positive regulation of interleukin-1 beta production, positive regulation of interleukin-1 production) and biological regulation (including signaling receptor activator activity, receptor ligand activity, cytokine activity), the majority of which are generally accredited to constituent parts of the development of CSU. A research containing 153 CSU patients suggested that the IL1 gene had a significant role in the susceptibility to CSU (<xref ref-type="bibr" rid="B46">46</xref>). Kasperska-Zajac et&#xa0;al. indicated that severity of systemic inflammation of CSU was related to elevated il-8 (<xref ref-type="bibr" rid="B47">47</xref>). The DEPRGs mainly participated in inflammatory pathways according to KEGG, likely the NF-kappa B signaling pathway, TNF signaling pathway, and NOD-like receptor signaling pathway. In addition, the result of DO further confirms the above. The DEPRGs were majorly enriched in inflammatory diseases like ulcerative colitis and fever. This advises that, to some extent, the DEPRGs could have a function to participate in the systemic inflammation of CSU.</p>
<p>Through the PPI network and module analyses, we identified five hub genes, namely, IL1B, TNF, IRF1 (all upregulated genes) and HMGB1, P2RX72 (both downregulated genes). They are the common inflammatory cytokines, but most of them have not been reported to be implicated in the development of CSU, so this would be a new finding. To fully explore the maladjustment of inflammatory cells of CSU, we executed immune infiltration analysis. It was found that CSU tissue owned a higher proportion of mast cells activated, but relatively lower ones of T cells CD4 naive, plasma cells, and B cells memory. Previous studies indicated that CSU was considered to be principally a mast cell-driven disease (<xref ref-type="bibr" rid="B48">48</xref>). Moreover, it has also been reported that the etiopathogenesis of CSU was closely associated with the dynamical unbalance of Th1/Th2 cells of CD4+T cells (<xref ref-type="bibr" rid="B49">49</xref>). However, since there have been few research reports, the relationship between CSU and plasma cells or/and B cells memory might be an interesting finding. Moreover, our research further showed that each of the hub genes (IL1B, TNF, IRF1, HMGB1, and P2RX7) was statistically related to major infiltration cells. Especially, IL1B and TNF were statistically and positively associated with mast cells activated, which suggests that they are related to maladjustment of inflammatory cells of CSU and might be its possible immunomodulation pivots. In addition, to reveal a systemically interactive modulation in CSU, we constructed a ceRNA network, in which there were 8 axes, such as IL1B/miR-21-5p/SNHG3, HMGB1/miR-34a-5p/TUG1, and P2RX7/miR-588/TUG1. It is worth noting that we also found that SNHG3 was significantly and negatively correlated with mast cells resting and plasma cells, and TUG1 was negatively related to mast cells activated, which further verified that the DEmRNA&#x2013;miRNA&#x2013;DElncRNA ceRNA network did have a critical role in maladjustment of inflammatory cells of CSU.</p>
<p>We chose IL1B to do further analysis for three reasons. First, it had the biggest fold change in hub genes. Second, it was verified by the validation set GSE57178. Third, both it and its upstream factor SNHG3 were related to the activation of mast cells. These indicated that IL1B could be in a more critical position in the development of CSU. IL1B (IL-1&#x3b2;) is a potent pro-inflammatory cytokine and plays a role in the innate and adaptive immunity of humans (<xref ref-type="bibr" rid="B50">50</xref>). Under the stimulation of immune response, inflammation, and infection, IL1B is released from monocytes, macrophages, and dendritic cells, affects local cells by paracrine, and targets distant cells <italic>via</italic> endocrine, ultimately leading to a series of inflammatory cascade responses like activation of immune cells and pyroptosis (<xref ref-type="bibr" rid="B51">51</xref>). Abnormal IL1B-related signaling pathways have been shown to be connected with some immune inflammatory diseases like SLE and UC (<xref ref-type="bibr" rid="B52">52</xref>, <xref ref-type="bibr" rid="B53">53</xref>). These were in agreement with the result of our research. We predicted that the protein subcellular localization of IL1B was extracellular, which is in accord with its intercellular regulation role. Moreover, it was found that IL1B was significantly correlated with familiar immune checkpoints in CSU, such as PDL1, CTLA4, TIM3, and TIGIT, which someway showed its role of immune regulation in CSU. Furthermore, IL1B was an overexpression examined in clinical CSU patients by ELISA, and the ROC curve analysis confirmed the dependability of its diagnostic value; the point was that mast cells were significantly activated in CSU and IL1B did correlate with it, which verified our bioinformatic analyses and suggests that IL1B could be a potential prognostic and diagnostic biomarker in CSU.</p>
<p>The NOD-like receptor signaling pathway was the major involved pathway according to the enrichment analysis of IL1B in a couple of databases. Moreover, NLRP3 is a subtype of NOD-like receptors and is famous for one of key pyroptosis cytokines; moreover, it is a well-known activator of IL1B (<xref ref-type="bibr" rid="B54">54</xref>). The study of Guo et&#xa0;al. showed that the increased expression of NLRP3 in mast cells leads to the activation of caspase-1 and ultimately to production and secretion of IL-1&#x3b2; in endometriosis (<xref ref-type="bibr" rid="B55">55</xref>). These are further supported in our work. In clinical CSU patients, NLRP3 was statistically overexpressed and related to the activation of mast cells. NLRP3 was also significantly correlated with IL1B, which might advise that the pyroptosis-related signaling pathway is activated in CSU and it might be related to the activation of mast cells. What is more, it might be <italic>via</italic> the NOD-like receptor signaling pathway, NLRP3, that IL1B participates in activation of mast cells. Moreover, we further identified 15 potential therapeutic drugs targeting IL1B, which provides a possible therapeutic strategy for CSU. Molecular docking revealed that the exact molecular binding makes this relationship more reliable.</p>
<p>Our study also had some limitations. We measured gene expression levels using sera from clinical CSU patients rather than tissues, which is not good enough but still clinically representative. Besides, we will perform the experiments <italic>in vivo</italic> and <italic>in vitro</italic> to further confirm our results in the future.</p>
<p>In sum, we identified 5 hub genes, IL1B, TNF, IRF1, HMGB1, and P2RX7, from pyroptosis-related genes, which are mainly involved in the inflammatory response and maladjustment of inflammatory cells of CSU. Particularly, ILB and its ceRNA axis might play a role in the activation of mast cells of CUS, and this might be achieved <italic>via</italic> the NOD-like receptor signaling pathway (NLRP3). Therefore, ILB and its related molecules might be potential key biomarkers in the development of CSU, and our study would provide a new perspective for the etiopathogenesis and therapeutic programs of CSU.</p>
</sec>
<sec id="s5" sec-type="data-availability">
<title>Data Availability Statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref ref-type="supplementary-material" rid="ST1">
<bold>Supplementary Material</bold>
</xref>.</p>
</sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics Statement</title>
<p>Written informed consent was obtained from the individual(s)&#x2019; and minor(s)&#x2019; legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author Contributions</title>
<p>SP and HF conceptualized the study design. SP and TZ drafted the manuscript. HF revised the manuscript. SP, SZ, QT, and YY collected data and performed the analysis. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This work was financed by the Hunan Provincial Health Commission scientific research projects (B2016229; B2019055; 202114051780) and Hunan Provincial Innovation Foundation for Postgraduate (CX20200547).</p>
</sec>
<sec id="s9" sec-type="COI-statement">
<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 id="s10" sec-type="disclaimer">
<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>
</body>
<back>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fimmu.2022.850993/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2022.850993/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="ST1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S1</label>
<caption>
<p>161 pyroptosis-related genes</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table_2.docx" id="ST2" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S2</label>
<caption>
<p>Population characteristics</p>
</caption>
</supplementary-material>
<supplementary-material xlink:href="Table_3.docx" id="ST3" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S3</label>
<caption>
<p>1033 DEmRNAs and 173 DElncRNAs of CSU</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="Table_4.xlsx" id="ST4" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet">
<label>Supplementary Table&#xa0;S4</label>
<caption>
<p>The DEGs of GSE72540</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="Table_5.docx" id="ST5" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S5</label>
<caption>
<p>The GO and KEGG enrichment of DEPRGs</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="Table_6.xls" id="ST6" mimetype="application/vnd.ms-excel">
<label>Supplementary Table&#xa0;S6</label>
<caption>
<p>PPI topology table</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="Table_7.docx" id="ST7" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S7</label>
<caption>
<p>Hub genes-mirRNAs</p>
</caption>
</supplementary-material>
  <supplementary-material xlink:href="Table_8.docx" id="ST8" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document">
<label>Supplementary Table&#xa0;S8</label>
<caption>
<p>MiRNAs-lncRNAs</p>
</caption>
</supplementary-material>
</sec>
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