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<journal-id journal-id-type="publisher-id">Front. Mol. Neurosci.</journal-id>
<journal-title>Frontiers in Molecular Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Mol. Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1662-5099</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnmol.2024.1493822</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Molecular Neuroscience</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Mapping the research landscape of microRNAs in pain: a comprehensive bibliometric analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes"><name><surname>Wang</surname> <given-names>Huaiming</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="author-notes" rid="fn0002"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes"><name><surname>Li</surname> <given-names>Qin</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="author-notes" rid="fn0002"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes"><name><surname>Zou</surname> <given-names>Jiang</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref><xref ref-type="author-notes" rid="fn0002"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author"><name><surname>Shu</surname> <given-names>Jinjun</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author"><name><surname>Zhang</surname> <given-names>Aimin</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author"><name><surname>Zhang</surname> <given-names>Hongwei</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author"><name><surname>Zhao</surname> <given-names>Qi</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author"><name><surname>Liu</surname> <given-names>Shunxin</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author" corresp="yes"><name><surname>Chen</surname> <given-names>Chan</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes"><name><surname>Chen</surname> <given-names>Guo</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Department of Anesthesiology, West China Hospital, Sichuan University</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital &#x0026; Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Sichuan Women&#x2019;s and Children&#x2019;s Hospital, Women&#x2019;s and Children&#x2019;s Hospital, Chengdu Medical College</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Guangxi University of Chinese Medicine</institution>, <addr-line>Nanning</addr-line>, <country>China</country></aff>
<aff id="aff5"><sup>5</sup><institution>The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University</institution>, <addr-line>Chengdu</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0003">
<p>Edited by: Lei Yu, The State University of New Jersey, United States</p>
</fn>
<fn fn-type="edited-by" id="fn0004">
<p>Reviewed by: Yayun Wang, Air Force Medical University, China</p>
<p>Shanchun Su, Louisiana State University, United States</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Chan Chen, <email>chenchan@scu.edu.cn</email>; Guo Chen, <email>chenguohx2023@163.com</email></corresp>
<fn fn-type="equal" id="fn0002"><p><sup>&#x2020;</sup>These authors have contributed equally to this work and share first authorship</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>17</volume>
<elocation-id>1493822</elocation-id>
<history>
<date date-type="received">
<day>09</day>
<month>09</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>10</day>
<month>12</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Wang, Li, Zou, Shu, Zhang, Zhang, Zhao, Liu, Chen and Chen.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Wang, Li, Zou, Shu, Zhang, Zhang, Zhao, Liu, Chen and Chen</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 id="sec1">
<title>Background and objectives</title>
<p>MicroRNAs (miRNAs) have demonstrated significant potential in pain medicine research, including mechanisms, diagnosis, and therapy. However, no relative bibliometric analysis has been performed to summarize the progress in this area quantitatively.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>Literature was retrieved from the Web of Science Core Collection online database. A total of 1,295 papers were retrieved between January 1, 2000 and September 21, 2023 and underwent visualization and analysis using R software [Library [bibliometrix] and biblioshiny packages], VOSviewer (version 1.6.18), CiteSpace software (version 6.2.R4), and the bibliometrics website (<ext-link xlink:href="http://bibliometric.com" ext-link-type="uri">http://bibliometric.com</ext-link>).</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>Publications in this field have increased annually since 2000, demonstrating growing research interest. China emerged as the most productive country, followed by the United States and Germany. Keyword analysis identified &#x201C;expression,&#x201D; &#x201C;neuropathic pain,&#x201D; and &#x201C;microRNAs&#x201D; as the most relevant keywords. Extensive collaboration among countries and institutions was also observed.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>The bibliometric analysis revealed a rapid growth of publications related to miRNAs and pain in the past 2 decades. Keywords analysis indicates that &#x201C;expression,&#x201D; &#x201C;neuropathic pain,&#x201D; and &#x201C;microRNA&#x201D; are the most frequently used words in this research field. However, more robust and globally recognized basic studies and clinical trials from prestigious journals are required.</p>
</sec>
</abstract>
<kwd-group>
<kwd>bibliometric analysis</kwd>
<kwd>microRNA</kwd>
<kwd>pain</kwd>
<kwd>Web of Science</kwd>
<kwd>miRNA</kwd>
</kwd-group>
<contract-num rid="cn1">ezmr2023-031</contract-num>
<contract-sponsor id="cn1">Enze Medical Research Projects for Pain Management, Bethune Charitable Foundation</contract-sponsor>
<counts>
<fig-count count="9"/>
<table-count count="6"/>
<equation-count count="0"/>
<ref-count count="31"/>
<page-count count="12"/>
<word-count count="5555"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Pain Mechanisms and Modulators</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<label>1</label>
<title>Introduction</title>
<p>Pain is an aversive sensory and emotional experience typically caused by, or resembling, an actual or potential tissue injury (<xref ref-type="bibr" rid="ref15">Raja et al., 2020</xref>). It often manifests as a comorbidity with various clinical complaints, including sensory discomfort, emotional disorders, cognitive impairment, and even social or family problems, causing significant distress to patients and their families (<xref ref-type="bibr" rid="ref15">Raja et al., 2020</xref>; <xref ref-type="bibr" rid="ref29">Zhang X. et al., 2023</xref>). Pain generation, progression, and management mechanisms have been extensively studied both macroscopically and microscopically.</p>
<p>MicroRNAs (miRNAs) are a class of small non-coding RNAs that can target numerous protein-coding genes and are involved in the evolutionary and pathological progression of animals and humans. By controlling post-transcriptional gene expression, miRNAs are involved in various diseases (<xref ref-type="bibr" rid="ref18">Saliminejad et al., 2019</xref>). Many studies have revealed that miRNAs can modulate pain, with many miRNAs being upregulated or downregulated in response to tissue or nerve injury (<xref ref-type="bibr" rid="ref11">Morchio et al., 2023</xref>; <xref ref-type="bibr" rid="ref23">Tao et al., 2023</xref>; <xref ref-type="bibr" rid="ref25">Vali et al., 2023</xref>). This modulation affects target miRNAs, either suppressing or promoting pain generation. These findings demonstrate that targeting miRNAs could be an essential pathway in pain pathophysiology and therapeutics (<xref ref-type="bibr" rid="ref29">Zhang X. et al., 2023</xref>; <xref ref-type="bibr" rid="ref18">Saliminejad et al., 2019</xref>; <xref ref-type="bibr" rid="ref9">Lopez-Gonzalez et al., 2017</xref>; <xref ref-type="bibr" rid="ref25">Vali et al., 2023</xref>; <xref ref-type="bibr" rid="ref17">Sakai and Suzuki, 2015</xref>; <xref ref-type="bibr" rid="ref22">Tao et al., 2018</xref>). Nevertheless, the articles or reviews rarely present an intuitive and visual mapping of the research trends and highlights in this specific field.</p>
<p>Visualized bibliometric analysis is a novel and efficient method for providing an understandable review of prominent publications over a specific period (<xref ref-type="bibr" rid="ref1">Chen et al., 2014</xref>). A recently published bibliometric analysis of the global study trends on neuropathic pain and epigenetics focused on the extensive function of genetics. It revealed some information and frontiers in epigenetics and neuropathic pain, especially DNA methylation, circular RNA, acetylation, and long non-coding RNA. However, a minuscule portion was associated with miRNA and the retrieve keyword mainly centered on all types of neuropathic pain (<xref ref-type="bibr" rid="ref31">Zhu et al., 2023</xref>). The literature on pain and miRNA has been rapidly increasing, but no systematic review of these publications has yet been conducted. Therefore, this study systematically reviewed publications to explore the development of this field, reviewed key publications, assessed current research focus, forecasted future trends, and provided an overview for researchers.</p>
</sec>
<sec sec-type="materials|methods" id="sec6">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Data retrieval strategy</title>
<p>The Web of Science (WOS) has been globally used for bibliometric analysis due to its high-quality literature (<xref ref-type="bibr" rid="ref30">Zhiguo et al., 2023</xref>). We searched the WOS Core Collection (WOSCC) on September 21, 2023, for publications related to pain and miRNA reported between January 1, 2000, and September 21, 2023. The retrieval formula was as follows: Topic Subject (TS)&#x202F;=&#x202F;(&#x201C;miRNA&#x201D; or &#x201C;microRNA&#x201D; or &#x201C;miRNAs&#x201D; or &#x201C;MicroRNA&#x201D; or &#x201C;RNA Micro&#x201D;) and (&#x201C;pain&#x201D; or &#x201C;ache&#x201D;), with the language limited to English. All included articles featured titles, abstracts, and keywords related to pain and miRNA.</p>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Data screening process</title>
<p>A total of 1,335 papers were retrieved. Two authors independently reviewed each paper to determine relevance and adherence to the inclusion criteria and exclusion criteria (listed below). If there was any uncertain paper after their evaluation, a third author was assigned to participate in a thorough discussion on that publication. If disagreement persisted, one of the corresponding authors reviewed the problematic publication to make the final decision. Lastly, 40 publications were excluded owing to the improper type of article. The final bibliometric analysis encompassed 1,295 articles. The literature retrieval and data screening process was illustrated in <xref ref-type="fig" rid="fig1">Figure 1</xref> and presented as a flowchart.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Process of article retrieval.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g001.tif"/>
</fig>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Inclusion criteria</title>
<p>
<list list-type="order">
<list-item>
<p>The study focused on pain and miRNA, with full text available.</p>
</list-item>
<list-item>
<p>The article type was either an article, review, or online publication.</p>
</list-item>
<list-item>
<p>The publication language was English.</p>
</list-item>
<list-item>
<p>The duration of the publication ranged from January 1, 2000, to September 21, 2023.</p>
</list-item>
<list-item>
<p>The literature was sourced from WOSCC.</p>
</list-item>
</list>
</p>
</sec>
<sec id="sec10">
<label>2.4</label>
<title>Exclusion criteria</title>
<p>
<list list-type="order">
<list-item>
<p>Topics not related to pain and miRNA.</p>
</list-item>
<list-item>
<p>Publication types including conference papers, abstracts, book chapters, theses, letters, corrections, withdrawn contributions, and reports.</p>
</list-item>
<list-item>
<p>Publications outside the defined time duration.</p>
</list-item>
</list>
</p>
</sec>
<sec id="sec11">
<label>2.5</label>
<title>Data analysis</title>
<p>To gain a comprehensive understanding of the current research on pain and miRNA, we used R software [library (bibliometrix) package and biblioshiny online tool] to visualize and analyze various aspects of the literature. First and foremost, the data quality was checked before the formal analysis by evaluating the completeness of bibliographic metadata (<xref ref-type="fig" rid="fig2">Figure 2</xref>). The following analysis included main information, annual scientific production, average citations per year, three-field plots, most relevant sources, authors, and affiliations, most locally cited sources, authors, documents, and references, affiliation production over time, corresponding author countries/regions, country scientific production, country production over time, most globally cited documents, most frequent words, tree map of words, word frequency over time, trending topics, clustering by coupling, co-occurrence network, thematic map, factorial analysis, co-citation network, collaboration network, and country collaboration maps.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>The completeness of bibliographic metadata.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g002.tif"/>
</fig>
<p>Co-authorship, co-citation, co-occurrence, citation patterns, and bibliographic coupling were visually studied using VOSviewer (version 1.6.18) and CiteSpace software (version 6.2.R4). Burst keywords were visualized and analyzed using CiteSpace to identify trends in pain and miRNA research.</p>
<p>The Bibliometrics website<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> was also used to analyze the annual publication output of the top 10 countries in this field and create a collaboration map among these countries.</p>
<p>Visualization maps are displayed as nodes and links. Nodes represented individual elements, such as keywords, countries, institutions, or authors, while linear links between nodes symbolized cooperation, co-citations, or occurrences among them. Nodes and links were color-coded to represent different years.</p>
</sec>
</sec>
<sec sec-type="results" id="sec12">
<label>3</label>
<title>Results</title>
<sec id="sec13">
<label>3.1</label>
<title>General publication information and global production growth trend</title>
<p>From January 1, 2000, to September 21, 2023, 6,191 authors from 52 countries contributed 1,295 publications across 528 international journals. The number of publications increased at an average annual rate of 23.44%, except for a decline in 2023 due to incomplete data retrieval for that year. Since 2010, there has been a significant increase in publications, indicating growing attention to research in pain and miRNAs and a positive outlook for advancements in pain medicine research (<xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p><bold>(A)</bold> General publication information. <bold>(B)</bold> Global production trend.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g003.tif"/>
</fig>
</sec>
<sec id="sec14">
<label>3.2</label>
<title>Analysis of country/region distribution</title>
<p>We investigated the distribution of publications across countries and discovered widespread interest in this research area. Authors from 52 different countries published papers on pain and miRNA, with Chinese authors dominating the field, contributing most of the papers, followed by researchers from the United States, Germany, Italy, Japan, the UK, South Korea, Canada, Israel, and Spain (<xref ref-type="fig" rid="fig4">Figure 4</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Distribution of countries by publication. <bold>(A)</bold> Annual publication of the top 10 countries; <bold>(B)</bold> The corporation map between the countries.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g004.tif"/>
</fig>
</sec>
<sec id="sec15">
<label>3.3</label>
<title>Keyword co-occurrence frequency and citation burst</title>
<p>Keyword co-occurrence frequency in the retrieved literature can provide insights into research interests, topics, and investigation trends in this scientific field. Using CiteSpace (version 6.2.R4), we generated keyword occurrence frequency statistics and visualized network mapping. We identified the top 10 frequent keywords: &#x201C;expression&#x201D; (<italic>n</italic>&#x202F;=&#x202F;431), &#x201C;neuropathic pain&#x201D; (<italic>n</italic>&#x202F;=&#x202F;311), &#x201C;micro RNAs&#x201D; (<italic>n</italic>&#x202F;=&#x202F;142), &#x201C;activation&#x201D; (<italic>n</italic>&#x202F;=&#x202F;121), &#x201C;cells&#x201D; (<italic>n</italic>&#x202F;=&#x202F;120), &#x201C;pain&#x201D; (<italic>n</italic>&#x202F;=&#x202F;114), &#x201C;inflammation&#x201D; (<italic>n</italic>&#x202F;=&#x202F;112), &#x201C;cancer&#x201D; (<italic>n</italic>&#x202F;=&#x202F;101), &#x201C;gene expression&#x201D; (<italic>n</italic>&#x202F;=&#x202F;98), and &#x201C;downregulation&#x201D; (<italic>n</italic>&#x202F;=&#x202F;95) (<xref ref-type="table" rid="tab1">Table 1</xref>).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Top 10 most frequently occurred keywords.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Keywords</th>
<th align="center" valign="top">Centrality</th>
<th align="center" valign="top">Count/frequency</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">1</td>
<td align="left" valign="top">Expression</td>
<td align="char" valign="top" char=".">0.1</td>
<td align="center" valign="top">431</td>
</tr>
<tr>
<td align="left" valign="top">2</td>
<td align="left" valign="top">Neuropathic pain</td>
<td align="char" valign="top" char=".">0.07</td>
<td align="center" valign="top">311</td>
</tr>
<tr>
<td align="left" valign="top">3</td>
<td align="left" valign="top">Micro RNAs</td>
<td align="char" valign="top" char=".">0.02</td>
<td align="center" valign="top">142</td>
</tr>
<tr>
<td align="left" valign="top">4</td>
<td align="left" valign="top">Activation</td>
<td align="char" valign="top" char=".">0.04</td>
<td align="center" valign="top">121</td>
</tr>
<tr>
<td align="left" valign="top">5</td>
<td align="left" valign="top">Cells</td>
<td align="char" valign="top" char=".">0.09</td>
<td align="center" valign="top">120</td>
</tr>
<tr>
<td align="left" valign="top">6</td>
<td align="left" valign="top">Pain</td>
<td align="char" valign="top" char=".">0.01</td>
<td align="center" valign="top">114</td>
</tr>
<tr>
<td align="left" valign="top">7</td>
<td align="left" valign="top">Inflammation</td>
<td align="char" valign="top" char=".">0.03</td>
<td align="center" valign="top">112</td>
</tr>
<tr>
<td align="left" valign="top">8</td>
<td align="left" valign="top">Cancer</td>
<td align="char" valign="top" char=".">0.02</td>
<td align="center" valign="top">101</td>
</tr>
<tr>
<td align="left" valign="top">9</td>
<td align="left" valign="top">Gene expression</td>
<td align="char" valign="top" char=".">0.13</td>
<td align="center" valign="top">98</td>
</tr>
<tr>
<td align="left" valign="top">10</td>
<td align="left" valign="top">Down regulation</td>
<td align="char" valign="top" char=".">0.05</td>
<td align="center" valign="top">95</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The keyword network highlighted both the frequency of co-occurrence and relationships between keywords. &#x201C;Expression,&#x201D; &#x201C;neuropathic pain,&#x201D; and &#x201C;microRNAs&#x201D; were the top three frequent keywords highlighting that neuropathic pain, a persistent and unyielding pain is a primary focus in research examining the function of miRNAs in pain modulation and treatment (<xref ref-type="fig" rid="fig5">Figure 5</xref>).</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Analysis of <bold>(A)</bold> keyword co-occurrence and <bold>(B)</bold> keyword cloud in pain and micro RNA research.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g005.tif"/>
</fig>
<p>Citation burst analysis of keywords revealed dynamic shifts in research topics over time. The top 25 keywords with the strongest citation bursts in pain and miRNA research are presented in <xref ref-type="fig" rid="fig6">Figure 6</xref>. Initially, gene identification and differential expression in pain-related diseases were the primary focus, particularly targeting the brain and spinal cord. However, in recent years, the focus has shifted to emerging topics such as small RNAs, long non-coding RNAs, and extracellular vesicles, reflecting evolving global research interests in the field of pain and miRNA.</p>
<fig position="float" id="fig6">
<label>Figure 6</label>
<caption>
<p>The top 25 keywords with the strongest citations burst in pain and miRNAs research.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g006.tif"/>
</fig>
</sec>
<sec id="sec16">
<label>3.4</label>
<title>Most productive and cited journal analysis</title>
<p>An online bibliometric analysis (see text footnote 1) identified 528 journals that published papers on pain and miRNAs during the analysis period. The most active journal was PLoS One (<italic>n</italic>&#x202F;=&#x202F;29), followed by International Journal of Molecular Sciences (<italic>n</italic>&#x202F;=&#x202F;27), Experimental and Therapeutic Medicine (<italic>n</italic>&#x202F;=&#x202F;25), Journal of Cellular Biochemistry (<italic>n</italic>&#x202F;=&#x202F;24), Molecular Pain (<italic>n</italic>&#x202F;=&#x202F;22), Frontiers in Molecular Neuroscience (<italic>n</italic>&#x202F;=&#x202F;21), Scientific Reports (<italic>n</italic>&#x202F;=&#x202F;19), Pain (<italic>n</italic>&#x202F;=&#x202F;18), Molecular Medicine Reports (<italic>n</italic>&#x202F;=&#x202F;18), and Molecular Neurobiology (<italic>n</italic>&#x202F;=&#x202F;17). The impact factors of the top 10 journals ranged from 2.7 to 7.4, with JCR (Journal Citation Report) quartiles distributed from Q1 to Q4 (<xref ref-type="table" rid="tab2">Table 2</xref>).</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Top 10 productive journals in the research of microRNAs and pain.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Journal</th>
<th align="center" valign="top">Counts</th>
<th align="center" valign="top">Citations</th>
<th align="center" valign="top">Citations per paper</th>
<th align="center" valign="top">Impact factors (2022)</th>
<th align="center" valign="top">JCI quartile (2022)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">1</td>
<td align="left" valign="top">PLoS One</td>
<td align="center" valign="top">29</td>
<td align="center" valign="top">175</td>
<td align="char" valign="top" char=".">6.03</td>
<td align="char" valign="top" char=".">3.7</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">2</td>
<td align="left" valign="top">International Journal of Molecular Science</td>
<td align="center" valign="top">27</td>
<td align="center" valign="top">57</td>
<td align="char" valign="top" char=".">2.11</td>
<td align="char" valign="top" char=".">5.6</td>
<td align="center" valign="top">Q2</td>
</tr>
<tr>
<td align="left" valign="top">3</td>
<td align="left" valign="top">Experimental and Therapeutic Medicine</td>
<td align="center" valign="top">25</td>
<td align="center" valign="top">47</td>
<td align="char" valign="top" char=".">1.88</td>
<td align="char" valign="top" char=".">2.7</td>
<td align="center" valign="top">Q4</td>
</tr>
<tr>
<td align="left" valign="top">4</td>
<td align="left" valign="top">Journal of Cellular Biochemistry</td>
<td align="center" valign="top">24</td>
<td align="center" valign="top">133</td>
<td align="char" valign="top" char=".">5.54</td>
<td align="char" valign="top" char=".">4</td>
<td align="center" valign="top">Q2</td>
</tr>
<tr>
<td align="left" valign="top">5</td>
<td align="left" valign="top">Molecular Pain</td>
<td align="center" valign="top">22</td>
<td align="center" valign="top">274</td>
<td align="char" valign="top" char=".">12.45</td>
<td align="char" valign="top" char=".">3.3</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">6</td>
<td align="left" valign="top">Frontiers in Molecular Neuroscience</td>
<td align="center" valign="top">21</td>
<td align="center" valign="top">161</td>
<td align="char" valign="top" char=".">7.67</td>
<td align="char" valign="top" char=".">4.8</td>
<td align="center" valign="top">Q2</td>
</tr>
<tr>
<td align="left" valign="top">7</td>
<td align="left" valign="top">Scientific Reports</td>
<td align="center" valign="top">19</td>
<td align="center" valign="top">67</td>
<td align="char" valign="top" char=".">3.53</td>
<td align="char" valign="top" char=".">4.6</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">8</td>
<td align="left" valign="top">Pain</td>
<td align="center" valign="top">18</td>
<td align="center" valign="top">188</td>
<td align="char" valign="top" char=".">10.44</td>
<td align="char" valign="top" char=".">7.4</td>
<td align="center" valign="top">Q1</td>
</tr>
<tr>
<td align="left" valign="top">9</td>
<td align="left" valign="top">Molecular Medicine Reports</td>
<td align="center" valign="top">18</td>
<td align="center" valign="top">43</td>
<td align="char" valign="top" char=".">2.39</td>
<td align="char" valign="top" char=".">3.4</td>
<td align="center" valign="top">Q4</td>
</tr>
<tr>
<td align="left" valign="top">10</td>
<td align="left" valign="top">Molecular Neurobiology</td>
<td align="center" valign="top">17</td>
<td align="center" valign="top">91</td>
<td align="char" valign="top" char=".">5.35</td>
<td align="char" valign="top" char=".">5.1</td>
<td align="center" valign="top">Q2</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Additionally, some articles were published in renowned global journals, including Science (<italic>n</italic>&#x202F;=&#x202F;1) and Nature Communications (<italic>n</italic>&#x202F;=&#x202F;3). The most cited journal was Molecular Pain (<italic>n</italic>&#x202F;=&#x202F;274), with an average of 12.45 citations per paper (<xref ref-type="table" rid="tab3">Table 3</xref>). The network of co-cited journals generated by VOSviewer (version 1.6.18) demonstrated strong communication and citation links among these journals (<xref ref-type="fig" rid="fig7">Figure 7</xref>).</p>
<fig position="float" id="fig7">
<label>Figure 7</label>
<caption>
<p>Network of co-citations between these journals. Diversified colored nodes represent different journals. The size of the node stands for the strength of the co-citations among journals.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g007.tif"/>
</fig>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Top 10 most cited journals in the research of microRNAs and pain.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Journal</th>
<th align="center" valign="top">Citations</th>
<th align="center" valign="top">Citations per paper</th>
<th align="center" valign="top">Impact factors (2022)</th>
<th align="center" valign="top">JCI quartile (2022)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">1</td>
<td align="left" valign="top">Molecular Pain</td>
<td align="center" valign="top">274</td>
<td align="char" valign="top" char=".">12.45</td>
<td align="center" valign="top">3.3</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">2</td>
<td align="left" valign="top">Pain</td>
<td align="center" valign="top">188</td>
<td align="char" valign="top" char=".">10.44</td>
<td align="center" valign="top">7.4</td>
<td align="center" valign="top">Q1</td>
</tr>
<tr>
<td align="left" valign="top">3</td>
<td align="left" valign="top">PLoS One</td>
<td align="center" valign="top">175</td>
<td align="char" valign="top" char=".">6.03</td>
<td align="center" valign="top">3.7</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">4</td>
<td align="left" valign="top">Frontiers in Molecular Neuroscience</td>
<td align="center" valign="top">161</td>
<td align="char" valign="top" char=".">7.67</td>
<td align="center" valign="top">4.8</td>
<td align="center" valign="top">Q2</td>
</tr>
<tr>
<td align="left" valign="top">5</td>
<td align="left" valign="top">Journal of Neuroinflammation</td>
<td align="center" valign="top">155</td>
<td align="char" valign="top" char=".">11.92</td>
<td align="center" valign="top">9.3</td>
<td align="center" valign="top">Q1</td>
</tr>
<tr>
<td align="left" valign="top">6</td>
<td align="left" valign="top">International Journal of Molecular Medicine</td>
<td align="center" valign="top">153</td>
<td align="char" valign="top" char=".">11.77</td>
<td align="center" valign="top">5.4</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">7</td>
<td align="left" valign="top">Biochemical and Biophysical Research Communications</td>
<td align="center" valign="top">147</td>
<td align="char" valign="top" char=".">11.31</td>
<td align="center" valign="top">3.1</td>
<td align="center" valign="top">Q4</td>
</tr>
<tr>
<td align="left" valign="top">8</td>
<td align="left" valign="top">Neurochemical Research</td>
<td align="center" valign="top">147</td>
<td align="char" valign="top" char=".">14.7</td>
<td align="center" valign="top">4.4</td>
<td align="center" valign="top">Q3</td>
</tr>
<tr>
<td align="left" valign="top">9</td>
<td align="left" valign="top">Journal of Cellular Biochemistry</td>
<td align="center" valign="top">133</td>
<td align="char" valign="top" char=".">5.54</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">Q2</td>
</tr>
<tr>
<td align="left" valign="top">10</td>
<td align="left" valign="top">Journal of Neuroscience</td>
<td align="center" valign="top">125</td>
<td align="char" valign="top" char=".">20.83</td>
<td align="center" valign="top">5.3</td>
<td align="center" valign="top">Q1</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec17">
<label>3.5</label>
<title>Analysis of author production, citation information</title>
<p>We analyzed author production and impact on pain and miRNA research over time using R software (Library [Bibliometrix] and Biblioshiny package). <xref ref-type="table" rid="tab4">Table 4</xref> and <xref ref-type="fig" rid="fig8">Figure 8</xref> summarize the output from the top 10 authors since 2000. Zhang Y. from the State Key Laboratory of NBC Protection for Civilians in Beijing, China, was the most productive researcher in this field, followed by Wang J. from China and Soreq H. from Israel. Of the top 10 most contributive authors, 80% were from China (<xref ref-type="table" rid="tab4">Table 4</xref>), highlighting significant interest and contributions to studying the complex relationship between miRNA and pain.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Authors&#x2019; accumulative total production from January 1st, 2000 to September 21st, 2023.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Authors</th>
<th align="left" valign="top">Countries</th>
<th align="center" valign="top">Articles</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">1</td>
<td align="left" valign="bottom">Zhang Y.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">33</td>
</tr>
<tr>
<td align="left" valign="bottom">2</td>
<td align="left" valign="bottom">Wang J.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">26</td>
</tr>
<tr>
<td align="left" valign="bottom">3</td>
<td align="left" valign="bottom">Soreq H.</td>
<td align="left" valign="bottom">Israel</td>
<td align="center" valign="bottom">21</td>
</tr>
<tr>
<td align="left" valign="bottom">4</td>
<td align="left" valign="bottom">Li Y.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">20</td>
</tr>
<tr>
<td align="left" valign="bottom">5</td>
<td align="left" valign="bottom">Zhang J.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">19</td>
</tr>
<tr>
<td align="left" valign="bottom">6</td>
<td align="left" valign="bottom">Liu Y.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">17</td>
</tr>
<tr>
<td align="left" valign="bottom">6</td>
<td align="left" valign="bottom">Wang L.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">17</td>
</tr>
<tr>
<td align="left" valign="bottom">7</td>
<td align="left" valign="bottom">Zhang L.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">16</td>
</tr>
<tr>
<td align="left" valign="bottom">8</td>
<td align="left" valign="bottom">Ajit S. K.</td>
<td align="left" valign="bottom">USA</td>
<td align="center" valign="bottom">15</td>
</tr>
<tr>
<td align="left" valign="bottom">8</td>
<td align="left" valign="bottom">Wang Y.</td>
<td align="left" valign="bottom">China</td>
<td align="center" valign="bottom">15</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig position="float" id="fig8">
<label>Figure 8</label>
<caption>
<p>Production by authors over time. The size of the circle represents the number of articles.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g008.tif"/>
</fig>
<p>The visualized network of co-cited authors by VOSviewer (version 1.6.18) revealed that Barel D. P., Zhang Y., and Sakai A. were the three most frequently cited researchers in this field, with active citation exchanges among them (<xref ref-type="fig" rid="fig9">Figure 9</xref>).</p>
<fig position="float" id="fig9">
<label>Figure 9</label>
<caption>
<p>Network of co-cited authors. Nodes represent authors, with their size indicating the number of citations and curved lines representing co-citations between different authors.</p>
</caption>
<graphic xlink:href="fnmol-17-1493822-g009.tif"/>
</fig>
<p>Besides, the citation information was analyzed using R software [Library [Bibliometrix] and Biblioshiny package]. The top 10 most-cited papers were listed in <xref ref-type="table" rid="tab5">Table 5</xref>, in which the total citations (TC), TC per year, and normalized TC of each paper were presented as well. We found that the phase I study of liposomal miR-34a mimic for solid tumor therapy was most cited, followed by another research of miR-16-based mimic minicell intravenous infusion in human for recurrent malignant pleural mesothelioma and the study of MRX34 treatment for advanced solid tumor. Although, these miRNA therapy were related to cancer, however, the adverse effects included chronic pain commonly.</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>List of the top 10 most-cited papers.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Paper</th>
<th align="center" valign="top">DOI</th>
<th align="center" valign="top">TC</th>
<th align="center" valign="top">TC per year</th>
<th align="center" valign="top">Normalized TC</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">1</td>
<td align="left" valign="bottom">Beg M. S. (2017). Invest. New Drug</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1007/s10637-016-0407-y" ext-link-type="uri">10.1007/s10637-016-0407-y</ext-link>
</td>
<td align="center" valign="bottom">534</td>
<td align="char" valign="bottom" char=".">66.75</td>
<td align="char" valign="bottom" char=".">12.73</td>
</tr>
<tr>
<td align="left" valign="bottom">2</td>
<td align="left" valign="bottom">van Zandwijk N. (2017). Lancet Oncol.</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1016/S1470-2045(17)30621-6" ext-link-type="uri">10.1016/S1470-2045(17)30621-6</ext-link>
</td>
<td align="center" valign="bottom">394</td>
<td align="char" valign="bottom" char=".">49.25</td>
<td align="char" valign="bottom" char=".">9.39</td>
</tr>
<tr>
<td align="left" valign="bottom">3</td>
<td align="left" valign="bottom">Hong D. V. S. (2020). Br. J. Cancer</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1038/s41416-020-0802-1" ext-link-type="uri">10.1038/s41416-020-0802-1</ext-link>
</td>
<td align="center" valign="bottom">344</td>
<td align="char" valign="bottom" char=".">68.80</td>
<td align="char" valign="bottom" char=".">20.94</td>
</tr>
<tr>
<td align="left" valign="bottom">4</td>
<td align="left" valign="bottom">Shaked I. (2009). Immunity</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1016/j.immuni.2009.09.019" ext-link-type="uri">10.1016/j.immuni.2009.09.019</ext-link>
</td>
<td align="center" valign="bottom">338</td>
<td align="char" valign="bottom" char=".">21.13</td>
<td align="char" valign="bottom" char=".">2.35</td>
</tr>
<tr>
<td align="left" valign="bottom">5</td>
<td align="left" valign="bottom">Sommer C. (2018). Pain</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1097/j.pain.0000000000001122" ext-link-type="uri">10.1097/j.pain.0000000000001122</ext-link>
</td>
<td align="center" valign="bottom">251</td>
<td align="char" valign="bottom" char=".">35.86</td>
<td align="char" valign="bottom" char=".">8.95</td>
</tr>
<tr>
<td align="left" valign="bottom">6</td>
<td align="left" valign="bottom">McDonald M. K. (2014). Pain</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1016/j.pain.2014.04.029" ext-link-type="uri">10.1016/j.pain.2014.04.029</ext-link>
</td>
<td align="center" valign="bottom">227</td>
<td align="char" valign="bottom" char=".">20.64</td>
<td align="char" valign="bottom" char=".">4.31</td>
</tr>
<tr>
<td align="left" valign="bottom">7</td>
<td align="left" valign="bottom">Descalzi G. (2015). Trends Neurosci.</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1016/j.tins.2015.02.001" ext-link-type="uri">10.1016/j.tins.2015.02.001</ext-link>
</td>
<td align="center" valign="bottom">216</td>
<td align="char" valign="bottom" char=".">21.60</td>
<td align="char" valign="bottom" char=".">4.68</td>
</tr>
<tr>
<td align="left" valign="bottom">8</td>
<td align="left" valign="bottom">Devaux Y. (2012). Clin. Chem.</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1373/clinchem.2011.173823" ext-link-type="uri">10.1373/clinchem.2011.173823</ext-link>
</td>
<td align="center" valign="bottom">209</td>
<td align="char" valign="bottom" char=".">16.08</td>
<td align="char" valign="bottom" char=".">4.46</td>
</tr>
<tr>
<td align="left" valign="bottom">9</td>
<td align="left" valign="bottom">Burney R. O. (2009). Mol. Hum. Reprod.</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1093/molehr/gap068" ext-link-type="uri">10.1093/molehr/gap068</ext-link>
</td>
<td align="center" valign="bottom">205</td>
<td align="char" valign="bottom" char=".">12.81</td>
<td align="char" valign="bottom" char=".">1.42</td>
</tr>
<tr>
<td align="left" valign="bottom">10</td>
<td align="left" valign="bottom">Park C. K. (2014). Neuron</td>
<td align="center" valign="bottom">
<ext-link xlink:href="https://doi.org/10.1016/j.neuron.2014.02.011" ext-link-type="uri">10.1016/j.neuron.2014.02.011</ext-link>
</td>
<td align="center" valign="bottom">205</td>
<td align="char" valign="bottom" char=".">18.64</td>
<td align="char" valign="bottom" char=".">3.89</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec18">
<label>3.6</label>
<title>Analysis of organizations and institutions</title>
<p>We conducted a systematic analysis of contributions from various organizations or institutions. Among the top 10 most productive scientific institutions, Nanjing Medical University, Xuzhou Medical University, and Soochow University ranked in the top 3 (<xref ref-type="table" rid="tab6">Table 6</xref>), and 80% of these top 10 institutions are in China, highlighting the dominant role of Chinese institutions in this research field. This promising result boosts the confidence of Chinese scientist in pain medicine and motivates them to intensify their efforts in studying the relationship between pain and miRNA.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Analysis of organizations and institutions.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Rank</th>
<th align="left" valign="top">Organizations</th>
<th align="center" valign="top">Documents</th>
<th align="center" valign="top">Citations</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom">1</td>
<td align="left" valign="bottom">Nanjing Medical University</td>
<td align="center" valign="bottom">37</td>
<td align="center" valign="bottom">784</td>
</tr>
<tr>
<td align="left" valign="bottom">2</td>
<td align="left" valign="bottom">Xuzhou Medical University</td>
<td align="center" valign="bottom">31</td>
<td align="center" valign="bottom">631</td>
</tr>
<tr>
<td align="left" valign="bottom">3</td>
<td align="left" valign="bottom">Soochow University</td>
<td align="center" valign="bottom">31</td>
<td align="center" valign="bottom">240</td>
</tr>
<tr>
<td align="left" valign="bottom">4</td>
<td align="left" valign="bottom">Shanghai Jiao Tong University</td>
<td align="center" valign="bottom">29</td>
<td align="center" valign="bottom">357</td>
</tr>
<tr>
<td align="left" valign="bottom">5</td>
<td align="left" valign="bottom">Huazhong University of Science and Technology</td>
<td align="center" valign="bottom">28</td>
<td align="center" valign="bottom">642</td>
</tr>
<tr>
<td align="left" valign="bottom">6</td>
<td align="left" valign="bottom">Southern Medical University</td>
<td align="center" valign="bottom">25</td>
<td align="center" valign="bottom">295</td>
</tr>
<tr>
<td align="left" valign="bottom">7</td>
<td align="left" valign="bottom">University of Carolina</td>
<td align="center" valign="bottom">23</td>
<td align="center" valign="bottom">449</td>
</tr>
<tr>
<td align="left" valign="bottom">8</td>
<td align="left" valign="bottom">Zhengzhou University</td>
<td align="center" valign="bottom">23</td>
<td align="center" valign="bottom">442</td>
</tr>
<tr>
<td align="left" valign="bottom">9</td>
<td align="left" valign="bottom">Hebrew University Jerusalem</td>
<td align="center" valign="bottom">22</td>
<td align="center" valign="bottom">1,221</td>
</tr>
<tr>
<td align="left" valign="bottom">10</td>
<td align="left" valign="bottom">Sun Yat-sen University</td>
<td align="center" valign="bottom">20</td>
<td align="center" valign="bottom">250</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="sec19">
<label>4</label>
<title>Discussion</title>
<p>We conducted a bibliometric analysis of scientific publications on the relationship between miRNA and neuropathic pain globally since 2000. Numerous articles have been published, and literature production kept a yearly growth, reflecting notable advances that have been made in understanding the relationship between miRNA and pain over the past two decades. Also, the establishment of extensive collaboration among different countries, and worldwide authors indicated an increase in global scientific concerns on the exploration of pain mechanisms.</p>
<p>The top three most frequently occurring keywords were &#x201C;expression,&#x201D; &#x201C;neuropathic pain,&#x201D; and &#x201C;micro RNA,&#x201D; which indicated that neuropathic pain was the most widely used disease model for the research of micro RNA and pain. The expression level of some microRNAs might be involved in the development and persistence of neuropathic pain. It&#x2019;s widely known that miRNA plays a crucial role in post-transcriptional gene regulation and has demonstrated its potential value in pain progression and prognosis, including chronic, inflammatory, and neuropathic pain (<xref ref-type="bibr" rid="ref9">Lopez-Gonzalez et al., 2017</xref>; <xref ref-type="bibr" rid="ref17">Sakai and Suzuki, 2015</xref>).</p>
<p>Indeed, the revealed pain mechanisms have suggested a close relationship between miRNAs and the proteins or genes they regulate (<xref ref-type="bibr" rid="ref22">Tao et al., 2018</xref>; <xref ref-type="bibr" rid="ref17">Sakai and Suzuki, 2015</xref>; <xref ref-type="bibr" rid="ref6">Jiangpan et al., 2016</xref>; <xref ref-type="bibr" rid="ref27">Zhang et al., 2021</xref>; <xref ref-type="bibr" rid="ref4">Hao et al., 2022</xref>; <xref ref-type="bibr" rid="ref28">Zhang et al., 2020</xref>; <xref ref-type="bibr" rid="ref12">Pan et al., 2018</xref>; <xref ref-type="bibr" rid="ref13">Ph&#x1EA1;m et al., 2020</xref>; <xref ref-type="bibr" rid="ref3">Favereaux et al., 2011</xref>; <xref ref-type="bibr" rid="ref20">Tan et al., 2015</xref>). For instance, <xref ref-type="bibr" rid="ref27">Zhang et al. (2021)</xref> discovered that miRNA-107 contributes to inflammatory pain by downregulating GLT-1 expression. <xref ref-type="bibr" rid="ref4">Hao et al. (2022)</xref> discovered that the miRNA-22-Mtf1 signaling axis in the dorsal horn is critical for inflammatory pain progression. In neuropathic pain, which was the second most frequently occurring keyword, several novel miRNAs have been identified in recent years (<xref ref-type="bibr" rid="ref11">Morchio et al., 2023</xref>). miRNA-103 was the first well-characterized miRNA in this field (<xref ref-type="bibr" rid="ref3">Favereaux et al., 2011</xref>). Researchers discovered that subunits with Ca V 1.2 L-type calcium channels directly targeted miRNA-103. Calcium transient modulation was also observed in cultured spinal neurons, and altering the level of miRNA-103 expression altered pain behaviors (<xref ref-type="bibr" rid="ref3">Favereaux et al., 2011</xref>). miRNA-128-3p was demonstrated to alleviate neuropathic pain by targeting ZEB1 through neuroinflammation inhibition (<xref ref-type="bibr" rid="ref28">Zhang et al., 2020</xref>). MiRNA-155, which regulates inflammation-associated diseases, was upregulated, with its inhibition suppressing proinflammatory cytokines expression in the spinal cord of a CCI neuropathic pain rats model (<xref ref-type="bibr" rid="ref20">Tan et al., 2015</xref>). miRNA-23a was identified to regulate neuropathic pain via TXNIP/NLRP3 inflammasome axis by directly targeting CXCR4 in a partial sciatic nerve ligation mouse model (<xref ref-type="bibr" rid="ref12">Pan et al., 2018</xref>). <xref ref-type="bibr" rid="ref13">Ph&#x1EA1;m et al. (2020)</xref> discovered that miRNA 146a-5p-encapsulated nanoparticles alleviate pain behaviors by inhibiting various inflammatory pathways in spinal microglia and reducing proinflammatory cytokine release. In the animal pain model, some experiments demonstrated that functional manipulation of miRNAs can suppress pain-related behavior in various pain diseases. Although these findings presented an extensive and dynamic change in microRNA expression in different pain models or animal studies, the strong relationship between pain development and miRNA changes was highlighted. This makes some promising miRNAs potential therapeutic molecular targets for pain medication for the high conservation of miRNAs and their target sequences among species (<xref ref-type="bibr" rid="ref17">Sakai and Suzuki, 2015</xref>).</p>
<p>Similarly, researches were also conducted on humans suffering from pain disease and uncovered the biomarker potential of miRNA. <xref ref-type="bibr" rid="ref10">Mari et al. (2019)</xref> found that miRNA-320 and miRNA-98 derived from circulating plasma were proven to successfully classify the pain type patients suffering in 70% of the cases. The circulating miRNA-320 was demonstrated to be responsible for post-traumatic pain by <xref ref-type="bibr" rid="ref8">Linnstaedt et al. (2018)</xref>. In our current visualized bibliometric study, further analysis of the most cited papers was performed, and we found that microRNA-21, miRNA-146a, miRNA-155, and miRNA-939 from peripheral were extensively studied in pain research for the critical role of inflammatory pain and neuropathic pain (<xref ref-type="bibr" rid="ref19">Sommer et al., 2017</xref>; <xref ref-type="bibr" rid="ref16">Ramanathan et al., 2019</xref>). Besides, spinal microglia-derived miRNA-124 and miRNA-155 were identified to serve critical roles in neuropathic pain (<xref ref-type="bibr" rid="ref21">Tang et al., 2021</xref>). These promising microRNAs share the role of biomarkers in chronic pain in basic research. However, as for the application of miRNAs in clinical trials or pain management, it must be acknowledged that there is still a long way to go and some major obstacles and barriers remain before miRNA or its targeted gene is used as a medication, which the drug delivery strategy and the specificity of microRNA are the greatest barriers (<xref ref-type="bibr" rid="ref9">Lopez-Gonzalez et al., 2017</xref>). To do so, the employment of viral vectors, as well as the incorporation of cholesterol molecules into a miRNA depressant or the sense strand of a miRNA mimic has been demonstrated to be efficacious strategies (<xref ref-type="bibr" rid="ref9">Lopez-Gonzalez et al., 2017</xref>). Recently, packaging miRNA into extracellular vesicles, such as exosomes, proved to be a novel maneuver and could be potentially used as a candidate analgesic method (<xref ref-type="bibr" rid="ref26">Zhang L. et al., 2023</xref>; <xref ref-type="bibr" rid="ref16">Ramanathan et al., 2019</xref>; <xref ref-type="bibr" rid="ref2">DaCunza et al., 2024</xref>; <xref ref-type="bibr" rid="ref7">Kumar et al., 2024</xref>). Besides, more highly specified miRNAs and human tissue-derived miRNA data are warranted among the numerous candidates. Whatever, these studies, together with our current visualized analysis highlight the significance of miRNA in pain and show the direction of pain research in the future.</p>
<p>Our bibliometric analysis generated another interesting finding, which revealed that Chinese researchers contributed the most productions to the publications about pain and miRNA. Our study uncovered that 80% of the top 10 productive organizations and the top 3 most prolific scientists were from China. These findings were consistent with the comment in Nature, which noted that China has surpassed the United States in the total number of scientific publications, becoming the largest global producer of scientific articles (<xref ref-type="bibr" rid="ref24">Tollefson, 2018</xref>). According to the recently announced Global Research Pulse report from Springer Nature (Springer Nature. 2024, August. China Impact Report), China is now the largest contributor to global research output. The production of scientific research is tightly related to the economic level of a country. China is world&#x2019;s second-largest economy. In 2015, the Chinese government allocated about $400 billion to research and development, with this investment continuing to grow (<xref ref-type="bibr" rid="ref24">Tollefson, 2018</xref>), among which, Natural Science Foundation of China (NSFC) provided great funding and basic research assistance from the national level. Significant national funding and support have encouraged a growing number of researchers to explore basic medicine and promote the achievement of international cooperation. A deep-going retrieve of the most productive author&#x2019;s publications revealed that the top 1 ranked author Y. Zhang and his team established extensive collaboration with several internationally renowned hospitals or institutes, such as the East Tennessee State University, Henry Ford Hospital, and the Department of Physics Oakland University (<xref ref-type="bibr" rid="ref5">Jia et al., 2018</xref>; <xref ref-type="bibr" rid="ref14">Qiu et al., 2015</xref>). Extensive international exchanges and cooperations and the collaborative and open research environment would promote the launch of more innovative and comprehensive global research.</p>
<sec id="sec20">
<label>4.1</label>
<title>Limitations</title>
<p>Meanwhile, it is necessary to acknowledge the limitations of our bibliometric study. First, it included only English-language publications retrieved from WOSCC, excluding documents from other databases written in non-English languages. Second, the literature search did not entirely cover 2023, resulting in a decline in publications and a relatively unclear depiction of the overall trend. Third, we used the subject phrase &#x201C;pain&#x201D; in the data retrieval, which may have excluded papers with titles including &#x201C;hurt&#x201D; or &#x201C;injury.&#x201D; These limitations highlight the need for further research.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec21">
<label>5</label>
<title>Conclusion</title>
<p>Our bibliometric study utilizes multiple visualized tools, including R language, bibliometric website, VOS viewer, and CiteSpace software, and fully uncovered the global tendency of research on the relationship between miRNA and pain by analyzing the number of publications, keywords, author data, countries, institutions, collaborations, citations, etc. The number of publications kept a steady growth, reflecting an increasing interest in and exploring the relationship between miRNAs and pain. Keywords analysis indicates that &#x201C;expression,&#x201D; &#x201C;neuropathic pain,&#x201D; and &#x201C;microRNA&#x201D; were the most frequently occurring words in this research field. Authors from China contributed to most publications. Among them, Yi Zhang was the most productive researcher. However, papers from prestigious journals were sparsely searched. More robust and globally recognized basic studies and clinical trials from renowned journals are demanded.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec22">
<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 authors.</p>
</sec>
<sec sec-type="author-contributions" id="sec23">
<title>Author contributions</title>
<p>HW: Conceptualization, Funding acquisition, Investigation, Methodology, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing, Software. QL: Data curation, Investigation, Methodology, Writing &#x2013; review &#x0026; editing. JZ: Project administration, Resources, Supervision, Writing &#x2013; review &#x0026; editing. AZ: Data curation, Formal analysis, Methodology, Software, Writing &#x2013; review &#x0026; editing. JS: Investigation, Project administration, Resources, Writing &#x2013; review &#x0026; editing. HZ: Data curation, Formal analysis, Writing &#x2013; review &#x0026; editing. QZ: Resources, Visualization, Writing &#x2013; original draft. SL: Data curation, Resources, Writing &#x2013; review &#x0026; editing. CC: Methodology, Project administration, Supervision, Writing &#x2013; review &#x0026; editing. GC: Methodology, Project administration, Supervision, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="funding-information" id="sec24">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Enze Medical Research Projects for Pain Management, Bethune Charitable Foundation (grant no. ezmr2023-031).</p>
</sec>
<ack>
<p>The authors thank Sandeep Bhushan from the Department of Cardio-Thoracic Surgery of Chengdu Second People&#x2019;s Hospital, Chengdu, China for his generous assistance in the data visualization.</p>
</ack>
<sec sec-type="COI-statement" id="sec25">
<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="sec26">
<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>
<fn-group>
<fn id="fn0001"><p><sup>1</sup><ext-link xlink:href="http://bibliometric.com" ext-link-type="uri">http://bibliometric.com</ext-link></p></fn>
</fn-group>
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