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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Oncol.</journal-id>
<journal-title>Frontiers in Oncology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Oncol.</abbrev-journal-title>
<issn pub-type="epub">2234-943X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fonc.2022.983892</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Research trends on anti-PD-1/PD-L1 immunotherapy for esophageal cancer: A bibliometric analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Yuanyuan</given-names>
</name>
<uri xlink:href="https://loop.frontiersin.org/people/1370745"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Feng</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1210180"/>
</contrib>
</contrib-group>
<aff id="aff1">
<institution>Department of Oncology, The First Affiliated Hospital of Zhengzhou University</institution>, <addr-line>Zhengzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Yibo Fan, University of Texas MD Anderson Cancer Center, United States</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Jinbo Zhao, Tangdu Hospital, China; Zhikuan Wang, Fifth Medical Center of the PLA General Hospital, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Feng Wang, <email xlink:href="mailto:fengw010@163.com">fengw010@163.com</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Oncology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>17</day>
<month>11</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>12</volume>
<elocation-id>983892</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>07</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>10</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Yang and Wang</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Yang and Wang</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>Objectives</title>
<p>The study aims to summarize publication characteristics of anti-programmed cell death protein 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) immunotherapy for esophageal cancer and create scientific maps to explore hotspots and emerging trends with bibliometric methods.</p>
</sec>
<sec>
<title>Methods</title>
<p>The publications between 2012 and 2021 were retrieved from the Web of Science Core Collection (WoSCC) on June 20, 2022. Bibliometric tools including HistCite, VOSviewer, and CiteSpace were used for statistical analysis. Data on the trend of the annual output, countries/regions, institutions, journals, authors, subject categories, keywords, and co-cited references were presented in this study.</p>
</sec>
<sec>
<title>Results</title>
<p>A total of 552 publications written by 3,623 authors of 872 institutions, 44 countries/regions in 250 journals were included in the bibliometric study. China, USA and Japan were the key countries in this field. Kato Ken, Bang Yung-Jue, <italic>Frontiers in Oncology</italic>, <italic>Journal of Clinical Oncology</italic> and Natl Canc Ctr were the top 1 productive author, co-cited author, productive journal, co-cited journal and prolific institution, respectively. The top 4 most present keywords were esophageal cancer, immunotherapy, esophageal squamous cell carcinoma and PD-L1. Neoadjuvant chemotherapy, response, PD-1 blockade and CD8<sup>+</sup> T cell were four latest research frontiers. The keywords reflected the progress from PD-1/PD-L1 expression to the clinical application of PD-1/PD-L1 inhibitors. The current researches mainly focus on neoadjuvant immunotherapy for esophageal cancer and development of biomarkers. Further research is warranted to determine effective predictive biomarkers or models, illustrate the molecular mechanism of combined treatment, and construct the optimal therapeutic strategy.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>This study visually analyzed the global trend and hotspots of anti-PD-1/PD-L1 immunotherapy for esophageal cancer over the past decade. The results could guide scientists to comprehensively understand the global frontiers and determine future directions.</p>
</sec>
</abstract>
<kwd-group>
<kwd>bibliometrics</kwd>
<kwd>anti-PD-1/PD-L1</kwd>
<kwd>immunotherapy</kwd>
<kwd>esophageal cancer</kwd>
<kwd>CiteSpace</kwd>
<kwd>HistCite</kwd>
<kwd>VOSviewer</kwd>
<kwd>Web of Science (WOS)</kwd>
</kwd-group>
<counts>
<fig-count count="8"/>
<table-count count="6"/>
<equation-count count="0"/>
<ref-count count="75"/>
<page-count count="14"/>
<word-count count="5757"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Esophageal cancer is the eighth most common cancer and the sixth major cause of cancer-related death worldwide (<xref ref-type="bibr" rid="B1">1</xref>). In 2020, the world witnessed about 604,100 new cases and 544,100 deaths, equaling to the age-standardized morbidity and mortality rates of 6.3/100,000 and 5.6/100,000, respectively (<xref ref-type="bibr" rid="B2">2</xref>). Advanced esophageal squamous cell carcinoma (ESCC) is one of devastating tumors with the 5-year survival rate lower than 5% (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>). The etiology of esophageal cancer is not completely clear. Recognized risk factors include genetic predisposition, gastroesophageal reflux disease, alcohol consumption, smoking and obesity (<xref ref-type="bibr" rid="B5">5</xref>). The alternative clinical treatment for esophageal cancer mainly depends on the stage of the tumor and the specific condition of patients. For esophageal cancer, multidisciplinary approach is an effective strategy for managing this disease, which involves the use of surgery, radiotherapy, chemotherapy, targeted therapy, immunotherapy and other treatments (<xref ref-type="bibr" rid="B6">6</xref>). In recent years, the emergence of immunotherapy has brought new hope for esophageal cancer. With the recognition of tumor immunotherapy, the application of immune checkpoint inhibitors (ICIs) has gradually shifted from the back-line and second-line treatments to first-line and even perioperative treatments. PD-1/PD-L1 inhibitors have been approved to be used for the first-line treatment of advanced esophageal cancer, which significantly improves patient prognosis (<xref ref-type="bibr" rid="B7">7</xref>). The response rate of ICI alone in esophageal cancer varied from 9.9 to 33.3% in the reported studies (<xref ref-type="bibr" rid="B8">8</xref>).</p>
<p>A large number of articles regarding anti-PD-1/PD-L1 immunotherapy for esophageal cancer have been published in the past decade. However, no systematic analysis of the data in the available articles has been performed. The increasing number of publications makes it more necessary to illustrate the state of the development by bibliometric methods (<xref ref-type="bibr" rid="B9">9</xref>). Bibliometric analysis consists of the quantification and visualization of the data by applying mathematics and statistics (<xref ref-type="bibr" rid="B10">10</xref>). In this way, the research trend of the area can be objectively shown. The bibliometric analysis provides researchers valuable information on the development of a specific field from a macro perspective. The most important data source is the Web of Science Core Collection (WoSCC) database (<xref ref-type="bibr" rid="B11">11</xref>).</p>
<p>Up to now, there is no published bibliometric study has systematically evaluated the anti-PD-1/PD-L1 immunotherapy for esophageal cancer from 2012 to 2021. In this work, the research tendency and hotspots of anti-PD-1/PD-L1 immunotherapy for esophageal cancer were visually analyzed using HistCite, VOSviewer, and CiteSpace. The aim was to identify the characteristics of publications, build collaboration networks, present hot words, reveal research frontiers and direct the follow-up work (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B13">13</xref>).</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Data source and search strategy</title>
<p>The literature retrieval was performed online using the WoSCC database, which is the most influential citation academic document database worldwide, in order to collect publications on anti-PD-1/PD-L1 immunotherapy for esophageal cancer (<xref ref-type="bibr" rid="B14">14</xref>). The search was performed on June 20, 2022 to ensure the same conditions and avoid the bias resulting from daily updates (<xref ref-type="bibr" rid="B15">15</xref>). Since all data were downloaded from the public database, no ethical approval was needed for this work. The following formula was used to perform the advanced search: TS = ((esophageal neoplasm OR esophagus neoplasm OR esophageal cancer OR esophagus cancer OR esophageal tumor OR esophagus tumor OR esophageal carcinoma OR esophagus carcinoma OR ESCC) AND (programmed cell death 1 receptor OR programmed cell death ligand 1 OR CD274 OR B7H1 OR PD-1 OR PD-L1 OR Nivolumab OR Pembrolizumab OR Lambrolizumab OR Avelumab OR Atezolizumab OR Nivolizumab OR Durvalumab OR Pidilizumab OR Cemiplimab OR Camrelizumab OR Sintilimab OR Tisleizumab OR Toripalimab)). The detailed retrieval process and analysis procedure are shown in <xref ref-type="fig" rid="f1">
<bold>Figure 1</bold>
</xref>.</p>
<fig id="f1" position="float">
<label>Figure 1</label>
<caption>
<p>Flow Diagram of detailed retrieval process and analysis procedure.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g001.tif"/>
</fig>
</sec>
<sec id="s2_2">
<title>Inclusion criteria and exclusion criteria</title>
<p>The inclusion criteria are as follows: (a) literature on anti-PD-1/PD-L1 immunotherapy for esophageal cancer; (b) literature types include articles and reviews; (c) literature published between 2012 and 2021; (d) literature indexed in WoSCC.</p>
<p>The exclusion criteria are as follows: (a) Unpublished documents; (b) Duplicate reports.</p>
</sec>
<sec id="s2_3">
<title>Statistical analysis</title>
<p>WoSCC was used to collect publications for bibliometric analysis and visualization. All the data retrieved from WoSCC were exported in plain text format. HistCite (Clarivate Analytics, Philadelphia, PA, the USA), VOSviewer 1.6.14 (Leiden University, Leiden, the Netherlands) and CiteSpace 5.3.R4 (Drexel University, Philadelphia, the USA) were used for statistical analysis (<xref ref-type="bibr" rid="B16">16</xref>). HistCite is a citation analysis software that summarizes and processes data quickly (<xref ref-type="bibr" rid="B17">17</xref>). In this study, annual output, language type, document type and total number of citations were analyzed by HistCite. VOSviewer is available for building and viewing bibliometric maps, and displays the results of the cluster analysis, including research characteristics, distribution and hotspots (<xref ref-type="bibr" rid="B18">18</xref>). The visual maps of countries/regions, institutions, journals, authors, keywords and references were generated by VOSviewer. CiteSpace, a Java application software, was used to explore the collaboration among countries/institutions/authors, identify co-cited authors/references, detect burst keywords and construct visualization maps (<xref ref-type="bibr" rid="B19">19</xref>). The software was effective in revealing the trends and dynamics of publications as well as capturing hotspots in a given research field (<xref ref-type="bibr" rid="B20">20</xref>). Due to its rich functions, CiteSpace has been widely used for bibliometric analysis. The CiteSpace parameters were as follows: time slicing (2012&#x2013;2021), years per slice (<xref ref-type="bibr" rid="B1">1</xref>), term source (all selection), term type (burst terms), node type (choose one at a time), links (strength: cosine; scope: within slices), selection criteria (top 50 objects), and pruning (pathfinder and pruning sliced networks).</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>General data and annual output</title>
<p>A total of 552 publications were included in the bibliometric study from 2012 to 2021. These publications were written by 3,623 authors of 872 institutions, 44 countries/regions in 250 journals, with 16,778 citations totally. All publications involved were made up of original articles (n = 410, 74.28%) and reviews (n = 142, 25.72%). The annual output generally maintained an increase trend in the past decade (<xref ref-type="fig" rid="f2">
<bold>Figure 2</bold>
</xref>). The most prolific year was 2021 with 216 publications, while the minimum annual output occurred in 2012 with one article. 2014 is the fastest growing year in the past decade. As regard the total citations, the figure peaked at 1,886 in 2018 and bottomed out in 2012.</p>
<fig id="f2" position="float">
<label>Figure 2</label>
<caption>
<p>
<bold>(A</bold>, <bold>B)</bold> The annual output, citations and growth rate presented by year from 2012 to 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g002.tif"/>
</fig>
</sec>
<sec id="s3_2">
<title>Countries/regions</title>
<p>A total of 44 countries/regions participated in the publication of anti-PD-1/PD-L1 immunotherapy for esophageal cancer in the last 10 years. Among them, China (n = 227, 32.76%) was the most prolific country/region, followed by the USA (n = 153, 22.08%) and Japan (n = 101, 14.57%). In terms of citations, the USA had the most total citations and France had the highest ratio of Citations/Paper. <xref ref-type="table" rid="T1">
<bold>Table 1</bold>
</xref> lists the top 8 most prolific countries/regions. A network map was constructed for countries with more than 5 publications. <xref ref-type="fig" rid="f3">
<bold>Figure 3</bold>
</xref> shows that the map had 18 nodes. The 3 largest nodes respectively represented China, the USA and Japan for their huge number of publications. The USA had the most active cooperation with others, with the strongest total link strength (TLS, TLS = 125). The closest cooperation was between China and the USA (TLS = 27).</p>
<table-wrap id="T1" position="float">
<label>Table 1</label>
<caption>
<p>The top 8 countries according to total publications from 2012 to 2022.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="center">Country</th>
<th valign="top" align="center">Number of Publications</th>
<th valign="top" align="center">Proportion (%)</th>
<th valign="top" align="center">Total Citations</th>
<th valign="top" align="center">Citations/Paper</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">China</td>
<td valign="top" align="center">227</td>
<td valign="top" align="center">32.76%</td>
<td valign="top" align="center">3583</td>
<td valign="top" align="center">15.78</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">USA</td>
<td valign="top" align="center">153</td>
<td valign="top" align="center">22.08%</td>
<td valign="top" align="center">4562</td>
<td valign="top" align="center">29.82</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="center">101</td>
<td valign="top" align="center">14.57%</td>
<td valign="top" align="center">2406</td>
<td valign="top" align="center">23.82</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">England</td>
<td valign="top" align="center">30</td>
<td valign="top" align="center">4.33%</td>
<td valign="top" align="center">1147</td>
<td valign="top" align="center">38.23</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Germany</td>
<td valign="top" align="center">25</td>
<td valign="top" align="center">3.61%</td>
<td valign="top" align="center">1145</td>
<td valign="top" align="center">45.80</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">South Korea</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">2.89%</td>
<td valign="top" align="center">738</td>
<td valign="top" align="center">36.90</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Italy</td>
<td valign="top" align="center">20</td>
<td valign="top" align="center">2.89%</td>
<td valign="top" align="center">480</td>
<td valign="top" align="center">24.00</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">France</td>
<td valign="top" align="center">17</td>
<td valign="top" align="center">2.45%</td>
<td valign="top" align="center">920</td>
<td valign="top" align="center">54.12</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data were retrieved from 552 publications with VOSviewer on June 20, 2022.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f3" position="float">
<label>Figure 3</label>
<caption>
<p>The co-authorship network visualization map of countries/regions. Larger nodes represent more publications of the term. Lines between nodes represent the connection between them.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g003.tif"/>
</fig>
</sec>
<sec id="s3_3">
<title>Institutions</title>
<p>A total of 872 institutions contributed to the research of anti-PD-1/PD-L1 immunotherapy for esophageal cancer. The top 10 most productive institutions from 2012 to 2021 are listed in <xref ref-type="table" rid="T2">
<bold>Table 2</bold>
</xref>. Natl Canc Ctr (Japan, 23 publications) and Zhengzhou Univ (China, 23 publications) tied first place, followed by Sun Yat Sen Univ (China, 21 publications), Natl Canc Ctr Hosp East (Japan, 19 publications) and Fudan Univ (China, 16 publications). The publications of the top 10 institutions accounted for more than 29% of the total publications. As regard the citations, Dana Farber Canc Inst (the USA, 1,108 citations) ranked first. Natl Canc Ctr Hosp East (Japan, 733 citations) and Natl Canc Ctr (Japan, 725 citations) came second and third, respectively. <xref ref-type="fig" rid="f4">
<bold>Figure 4</bold>
</xref> shows the co-authorship network among institutions with 10 or more publications. It is evident that institutions in the same district always closely cooperate with each other. Natl Canc Ctr Hosp East (TLS = 78) had the most active cooperation with others. The closest cooperation was between Natl Canc Ctr Hosp East and Natl Canc Ctr (TLS = 14).</p>
<table-wrap id="T2" position="float">
<label>Table 2</label>
<caption>
<p>The top 10 most productive institutions from 2012 to 2021.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="center">The name of institution</th>
<th valign="top" align="center">Publications</th>
<th valign="top" align="center">Citations</th>
<th valign="top" align="center">Location</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">Natl Canc Ctr</td>
<td valign="top" align="center">23</td>
<td valign="top" align="center">725</td>
<td valign="top" align="left">Japan</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Zhengzhou Univ</td>
<td valign="top" align="center">23</td>
<td valign="top" align="center">283</td>
<td valign="top" align="left">China</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Sun Yat Sen Univ</td>
<td valign="top" align="center">21</td>
<td valign="top" align="center">168</td>
<td valign="top" align="left">China</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Natl Canc Ctr Hosp East</td>
<td valign="top" align="center">19</td>
<td valign="top" align="center">733</td>
<td valign="top" align="left">Japan</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Fudan Univ</td>
<td valign="top" align="center">16</td>
<td valign="top" align="center">85</td>
<td valign="top" align="left">China</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Mem Sloan Kettering Canc Ctr</td>
<td valign="top" align="center">15</td>
<td valign="top" align="center">488</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Soochow Univ</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">362</td>
<td valign="top" align="left">China</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">Dana Farber Canc Inst</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">1108</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">9</td>
<td valign="top" align="left">Chinese Acad Med Sci</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">284</td>
<td valign="top" align="left">China</td>
</tr>
<tr>
<td valign="top" align="left">10</td>
<td valign="top" align="left">Chinese Acad Med Sci &amp; Peking Union Med Coll</td>
<td valign="top" align="center">12</td>
<td valign="top" align="center">58</td>
<td valign="top" align="left">China</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data were retrieved from 552 publications with VOSviewer on June 20, 2022.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f4" position="float">
<label>Figure 4</label>
<caption>
<p>The co-authorship network visualization map of institutions.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g004.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>Journals and co-cited journals</title>
<p>All the 552 papers were published in 250 journals. The top 10 prolific journals and co-cited journals are listed in <xref ref-type="table" rid="T3">
<bold>Table 3</bold>
</xref>. The 10 most prolific journals published 164 papers, accounting for 29.71% papers involved in this study. The IF of these journals ranged from 3.111 to 13.801, half of which were higher than 6. Among these journals, <italic>Frontiers in Oncology</italic> (IF = 5.738; 22 publications) had most publications, followed by <italic>Cancer Science</italic> (IF = 6.518; 14 publications) and <italic>Journal for Immunotherapy of Cancer</italic> (IF = 12.469; 13 publications). The top 3 co-cited journals were as follows: <italic>Journal of Clinical Oncology</italic> (IF = 50.717; 1,788 co-citations), <italic>New England Journal of Medicine</italic> (IF = 176.079; 1,232 co-citations) and <italic>Lancet Oncology</italic> (IF = 54.433; 996 co-citations). The co-citations of the 50% of the listed journals were greater than 700 and 7 of the top 10 co-cited journals had IF higher than 50.</p>
<table-wrap id="T3" position="float">
<label>Table 3</label>
<caption>
<p>The top 10 journals and co-cited journals on anti-PD-1/PD-L1 immunotherapy for esophageal cancer from 2012 to 2021.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="center">Journal</th>
<th valign="top" align="center">Publication number</th>
<th valign="top" align="center">Citation</th>
<th valign="top" align="center">IF#</th>
<th valign="top" align="center">Co-cited journal</th>
<th valign="top" align="center">Co-citation</th>
<th valign="top" align="center">IF</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">Frontiers in Oncology</td>
<td valign="top" align="center">22</td>
<td valign="top" align="center">165</td>
<td valign="top" align="center">5.738</td>
<td valign="top" align="left">Journal of Clinical Oncology</td>
<td valign="top" align="center">1788</td>
<td valign="top" align="center">50.717</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Cancer Science</td>
<td valign="top" align="center">14</td>
<td valign="top" align="center">434</td>
<td valign="top" align="center">6.518</td>
<td valign="top" align="left">New England Journal Of Medicine</td>
<td valign="top" align="center">1232</td>
<td valign="top" align="center">176.079</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Journal for Immunotherapy of Cancer</td>
<td valign="top" align="center">13</td>
<td valign="top" align="center">298</td>
<td valign="top" align="center">12.469</td>
<td valign="top" align="left">Lancet Oncology</td>
<td valign="top" align="center">996</td>
<td valign="top" align="center">54.433</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Annals of Translational Medicine</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">54</td>
<td valign="top" align="center">3.616</td>
<td valign="top" align="left">Clinical Cancer Research</td>
<td valign="top" align="center">853</td>
<td valign="top" align="center">13.801</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Cancers</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">37</td>
<td valign="top" align="center">6.575</td>
<td valign="top" align="left">Annals of Oncology</td>
<td valign="top" align="center">787</td>
<td valign="top" align="center">51.769</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Future Oncology</td>
<td valign="top" align="center">11</td>
<td valign="top" align="center">207</td>
<td valign="top" align="center">3.674</td>
<td valign="top" align="left">Lancet</td>
<td valign="top" align="center">591</td>
<td valign="top" align="center">202.731</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Cancer Immunology Immunotherapy</td>
<td valign="top" align="center">10</td>
<td valign="top" align="center">142</td>
<td valign="top" align="center">6.630</td>
<td valign="top" align="left">Oncotarget</td>
<td valign="top" align="center">553</td>
<td valign="top" align="center">&#x2014;&#x2014;</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">Clinical Cancer Research</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">812</td>
<td valign="top" align="center">13.801</td>
<td valign="top" align="left">Nature</td>
<td valign="top" align="center">552</td>
<td valign="top" align="center">69.504</td>
</tr>
<tr>
<td valign="top" align="left">9</td>
<td valign="top" align="left">Oncotargets and Therapy</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">456</td>
<td valign="top" align="center">4.345</td>
<td valign="top" align="left">Cancer Research</td>
<td valign="top" align="center">502</td>
<td valign="top" align="center">13.312</td>
</tr>
<tr>
<td valign="top" align="left">10</td>
<td valign="top" align="left">Oncology Letters</td>
<td valign="top" align="center">8</td>
<td valign="top" align="center">80</td>
<td valign="top" align="center">3.111</td>
<td valign="top" align="left">Science</td>
<td valign="top" align="center">352</td>
<td valign="top" align="center">63.714</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data were retrieved from 552 publications with VOSviewer on June 20, 2022.</p>
</fn>
<fn>
<p>#: Abbreviation for Impact Factor.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Authors and co-cited authors</title>
<p>A total of 3,623 authors contributed to the involved publications. <xref ref-type="table" rid="T4">
<bold>Table 4</bold>
</xref> shows that the top 10 authors were all from Japan. Among them, Kato Ken with 16 publications and 410 citations was the most productive author, followed by Kojima Takashi (12 publications, 440 citations) and Doi Toshihiko (10 publications, 394 citations). As regard the co-cited authors, Bang Yung-Jue from South Korea ranked first with 197 co-citations, followed by Kato Ken (193 co-citations) and Fuchs Charles S. (184 co-citations).</p>
<table-wrap id="T4" position="float">
<label>Table 4</label>
<caption>
<p>The top 10 prolific authors and co-cited authors on anti-PD-1/PD-L1 immunotherapy for esophageal cancer research from 2012 to 2021.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">
</th>
<th valign="top" colspan="4" align="left">Author</th>
<th valign="top" colspan="3" align="left">Co-cited authors</th>
</tr>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="left">Name</th>
<th valign="top" align="left">Publications</th>
<th valign="top" align="left">Citations</th>
<th valign="top" align="left">Country</th>
<th valign="top" align="left">Name</th>
<th valign="top" align="left">Co-citations</th>
<th valign="top" align="left">Country</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">Kato Ken</td>
<td valign="top" align="left">16</td>
<td valign="top" align="left">410</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Bang Yung-Jue</td>
<td valign="top" align="left">197</td>
<td valign="top" align="left">South Korea</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Kojima Takashi</td>
<td valign="top" align="left">12</td>
<td valign="top" align="left">440</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Kato Ken</td>
<td valign="top" align="left">193</td>
<td valign="top" align="left">Japan</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Doi Toshihiko</td>
<td valign="top" align="left">10</td>
<td valign="top" align="left">394</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Fuchs Charles S.</td>
<td valign="top" align="left">184</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Baba Hideo</td>
<td valign="top" align="left">8</td>
<td valign="top" align="left">222</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Shah Manish A.</td>
<td valign="top" align="left">163</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Yoshida Naoya</td>
<td valign="top" align="left">8</td>
<td valign="top" align="left">222</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Le Dung T.</td>
<td valign="top" align="left">158</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Doki Yuichiro</td>
<td valign="top" align="left">8</td>
<td valign="top" align="left">114</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Janjigian Yelena Y.</td>
<td valign="top" align="left">154</td>
<td valign="top" align="left">the USA</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Kono Koji</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">264</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Shitara Kohei</td>
<td valign="top" align="left">134</td>
<td valign="top" align="left">Japan</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">Ishimoto Takatsugu</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">221</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Kojima Takashi</td>
<td valign="top" align="left">125</td>
<td valign="top" align="left">Japan</td>
</tr>
<tr>
<td valign="top" align="left">9</td>
<td valign="top" align="left">Iwatsuki Masaaki</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">221</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Kang Y.K.</td>
<td valign="top" align="left">117</td>
<td valign="top" align="left">South Korea</td>
</tr>
<tr>
<td valign="top" align="left">10</td>
<td valign="top" align="left">Mori Masaki</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">194</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">Topalian Suzanne L.</td>
<td valign="top" align="left">117</td>
<td valign="top" align="left">the USA</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data were retrieved from 552 publications with VOSviewer on June 20, 2022.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>VOSviewer analyzed the information of authors and co-cited authors, then visualized it in a network map to explore influential researchers and potential collaborators (<xref ref-type="fig" rid="f5">
<bold>Figure 5</bold>
</xref>) (<xref ref-type="bibr" rid="B21">21</xref>). The 42 authors with more than 5 publications formed several clusters and almost no collaboration was present among the clusters. The linkages among the authors were clearly less robust. However, the linkages among authors from same cluster were relatively close. When it comes to the co-cited authors, the minimum number of co-citations was set as 60. Unlike authors, the collaborations among 31 co-cited authors were quite active.</p>
<fig id="f5" position="float">
<label>Figure 5</label>
<caption>
<p>
<bold>(A)</bold> Co-authorship network visualization map of authors. <bold>(B)</bold> Co-citation network visualization map of authors.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g005.tif"/>
</fig>
</sec>
<sec id="s3_6">
<title>Subject categories</title>
<p>In the present study, CiteSpace was used to analyze the information regarding publication categories and construct a knowledge map (<xref ref-type="fig" rid="f6">
<bold>Figure 6</bold>
</xref>). The larger node represented more publications of the term. Nodes with high centrality were usually considered as pivotal points in the field (<xref ref-type="bibr" rid="B22">22</xref>). In this work, the top 5 subject categories were selected according to the publication number and centrality. <xref ref-type="table" rid="T5">
<bold>Table 5</bold>
</xref> shows that ONCOLOGY and IMMUNOLOGY ranked first and second, respectively.</p>
<fig id="f6" position="float">
<label>Figure 6</label>
<caption>
<p>The visualization map of subject categories. The tree ring-shaped nodes represented different subject categories. The lines between two nodes meant co-occurrence. The area of the nodes referred to the number of publications. Nodes with high centrality were deemed as the hot field.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g006.tif"/>
</fig>
<table-wrap id="T5" position="float">
<label>Table 5</label>
<caption>
<p>Top 5 subject categories in terms of publication number and centrality related to anti-PD-1/PD-L1 immunotherapy for esophageal cancer.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="center">Publications</th>
<th valign="top" align="center">Category</th>
<th valign="top" align="center">Centrality</th>
<th valign="top" align="center">Category</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="center">539</td>
<td valign="top" align="left">ONCOLOGY</td>
<td valign="top" align="center">1.32</td>
<td valign="top" align="left">ONCOLOGY</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="center">94</td>
<td valign="top" align="left">IMMUNOLOGY</td>
<td valign="top" align="center">0.82</td>
<td valign="top" align="left">RESEARCH &amp; EXPERIMENTAL MEDICINE</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="center">74</td>
<td valign="top" align="left">RESEARCH &amp; EXPERIMENTAL MEDICINE</td>
<td valign="top" align="center">0.72</td>
<td valign="top" align="left">CELL BIOLOGY</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="center">74</td>
<td valign="top" align="left">GENERAL &amp; INTERNAL MEDICINE</td>
<td valign="top" align="center">0.66</td>
<td valign="top" align="left">PATHOLOGY</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="center">71</td>
<td valign="top" align="left">GASTROENTEROLOGY &amp; HEPATOLOGY</td>
<td valign="top" align="center">0.42</td>
<td valign="top" align="left">SURGERY</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Data were retrieved from 552 publications with CiteSpaceV on June 20, 2022.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_7">
<title>Keywords</title>
<p>High-frequency keywords represent the hot topics in a particular field. Fifty-nine keywords with more than 5 occurrences were extracted from 552 publications. The top 4 keywords with most occurrences were listed as follows: esophageal cancer (n = 123), immunotherapy (n = 119), esophageal squamous cell carcinoma (n = 89), and PD-L1 (n = 79). VOSviewer was used to construct the network map of keywords, including esophageal cancer, immunotherapy, esophageal squamous cell carcinoma, PD-L1, PD-1, prognosis and so on (<xref ref-type="fig" rid="f7">
<bold>Figure 7A</bold>
</xref>).</p>
<fig id="f7" position="float">
<label>Figure 7</label>
<caption>
<p>
<bold>(A)</bold> The co-occurrence network visualization map of keywords. Keywords in the same color represent were sorted into the same cluster. <bold>(B)</bold> The top 34 keywords with the strongest citation bursts from 2012 to 2021. The red segment of the blue line denoted the burst duration of a keyword.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g007.tif"/>
</fig>
<p>CiteSpace was used to identify and analyze keywords with citation bursts, thereby indicating the research hotspots and emerging trends over a period time. The minimum burst duration was set as 1 year. The top 34 keywords with the strongest citation burst are listed in <xref ref-type="fig" rid="f7">
<bold>Figure 7B</bold>
</xref>. Among them, clinical significance had the highest burst strength (8.44). Response, PD-1 blockade, CD8<sup>+</sup> T cell, and melanoma were the four keywords with the highest burst strength from 2018 to 2019. Neoadjuvant chemotherapy (NCT) was the top 1 keyword with the strongest citation bursts recently.</p>
</sec>
<sec id="s3_8">
<title>Co-cited references</title>
<p>Co-cited reference is regarded as one of the most valuable indicators in bibliometrics that displays the key landmark articles of this field (<xref ref-type="bibr" rid="B23">23</xref>&#x2013;<xref ref-type="bibr" rid="B25">25</xref>). <xref ref-type="table" rid="T6">
<bold>Table 6</bold>
</xref> lists the top 10 co-cited references. Among them, 8 articles were clinical trials, 2 were original articles. The article written by Freddie Bray et al. published in <italic>CA Cancer J Clin</italic> ranked first (n = 111) (<xref ref-type="bibr" rid="B26">26</xref>), followed by a clinical trial written by Yoon-Koo Kang et al. in <italic>Lancet</italic> (n = 99) (<xref ref-type="bibr" rid="B27">27</xref>) and another clinical trial written by Ken Kato et al. in <italic>Lancet Oncol</italic> (n = 97) (<xref ref-type="bibr" rid="B28">28</xref>). A co-citation network map was created using articles with more than 40 co-citations and explored the connection among these articles. The map contained 27 nodes, which clearly indicated the scientific relevance among these references. <xref ref-type="fig" rid="f8">
<bold>Figure 8A</bold>
</xref> shows that the largest node represented the most co-cited reference. &#x201c;Yoon-Koo Kang, 2017, <italic>Lancet</italic>, V390, P2461&#x201d; (TLS = 606) (<xref ref-type="bibr" rid="B27">27</xref>) had the most active association with other references, followed by &#x201c;Charles S Fuchs, 2018, <italic>JAMA Oncol</italic>, V4&#x201d; (TLS = 529) (<xref ref-type="bibr" rid="B32">32</xref>) and &#x201c;Manish A Shah, 2019, <italic>JAMA Oncol</italic>, V5, P546&#x201d; (TLS = 480) (<xref ref-type="bibr" rid="B30">30</xref>).</p>
<table-wrap id="T6" position="float">
<label>Table 6</label>
<caption>
<p>The top 10 co-cited reference from 2012 to 2021.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Rank</th>
<th valign="top" align="center">Co-cited reference</th>
<th valign="top" align="center">Count</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">1</td>
<td valign="top" align="left">Freddie Bray, 2018, CA Cancer J Clin, V68, P394 (<xref ref-type="bibr" rid="B26">26</xref>)</td>
<td valign="top" align="center">111</td>
</tr>
<tr>
<td valign="top" align="left">2</td>
<td valign="top" align="left">Yoon-Koo Kang, 2017, Lancet, V390, P2461 (<xref ref-type="bibr" rid="B27">27</xref>)</td>
<td valign="top" align="center">99</td>
</tr>
<tr>
<td valign="top" align="left">3</td>
<td valign="top" align="left">Ken Kato, 2019, Lancet Oncol, V20, P1506 (<xref ref-type="bibr" rid="B28">28</xref>)</td>
<td valign="top" align="center">97</td>
</tr>
<tr>
<td valign="top" align="left">4</td>
<td valign="top" align="left">Toshihiro Kudo, 2017, Lancet Oncol, V18, P631 (<xref ref-type="bibr" rid="B29">29</xref>)</td>
<td valign="top" align="center">91</td>
</tr>
<tr>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Manish A Shah, 2019, JAMA Oncol, V5, P546 (<xref ref-type="bibr" rid="B30">30</xref>)</td>
<td valign="top" align="center">83</td>
</tr>
<tr>
<td valign="top" align="left">6</td>
<td valign="top" align="left">Dung T Le, 2015, New Engl J Med, V372, P2509 (<xref ref-type="bibr" rid="B31">31</xref>)</td>
<td valign="top" align="center">81</td>
</tr>
<tr>
<td valign="top" align="left">7</td>
<td valign="top" align="left">Charles S Fuchs, 2018, JAMA Oncol, V4 (<xref ref-type="bibr" rid="B32">32</xref>)</td>
<td valign="top" align="center">79</td>
</tr>
<tr>
<td valign="top" align="left">8</td>
<td valign="top" align="left">Yuichiro Ohigashi, 2005, Clin Cancer Res, V11, P2947 (<xref ref-type="bibr" rid="B33">33</xref>)</td>
<td valign="top" align="center">76</td>
</tr>
<tr>
<td valign="top" align="left">9</td>
<td valign="top" align="left">Yung Jue Bang, 2010, Lancet, V376, P1302 (<xref ref-type="bibr" rid="B34">34</xref>)</td>
<td valign="top" align="center">74</td>
</tr>
<tr>
<td valign="top" align="left">10</td>
<td valign="top" align="left">Suzanne L Topalian, 2012, New Engl J Med, V366, P2443 (<xref ref-type="bibr" rid="B35">35</xref>)</td>
<td valign="top" align="center">73</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="f8" position="float">
<label>Figure 8</label>
<caption>
<p>
<bold>(A)</bold> The co-citation network visualization map of references from 2012 to 2021. <bold>(B)</bold> The top 33 references with the strongest citation bursts from 2012 to 2021. The red segment of the blue line denoted the burst duration of a keyword.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-12-983892-g008.tif"/>
</fig>
<p>References with citation bursts refer to those that are frequently cited during certain a period of time (<xref ref-type="bibr" rid="B36">36</xref>). CiteSpace was used to perform references with citation bursts, and the minimum burst duration was set as 1 year. The blue line in <xref ref-type="fig" rid="f8">
<bold>Figure 8B</bold>
</xref> represents the timeline in years, while the red line represents the time range in which a reference had citation burst (<xref ref-type="bibr" rid="B37">37</xref>). The burst strength of the top 33 references ranged from 4.05 to 14.25. Among them, &#x201c;Topalian SL, 2012, <italic>New Engl J Med</italic>, V366, P2443 (<xref ref-type="bibr" rid="B35">35</xref>)&#x201c; had the highest burst strength (14.25), which ranked tenth in the list of co-citations, indicating the great influence of this study. The article assessed the antitumor activity and safety of anti&#x2013;PD-1 antibody in cancer, showing that the adverse-event profile does not appear to preclude its use. &#x201c;Kudo T, 2017, Lancet Oncol, V18, P631&#x201d; (<xref ref-type="bibr" rid="B29">29</xref>), &#x201c;Doi T, 2018, <italic>J Clin Oncol</italic>, V36, P61&#x201d; (<xref ref-type="bibr" rid="B38">38</xref>) and &#x201c;Jiang YB, 2017, <italic>Oncotarget</italic>, V8, P30175&#x201d; (<xref ref-type="bibr" rid="B39">39</xref>) were 3 co-cited references with recent bursts. Toshihiro Kudo et al. conducted a phase II clinical trial and suggested that nivolumab exhibited favorable activity and controllable safety in ESCC (<xref ref-type="bibr" rid="B29">29</xref>). Toshihiko Doi et al. reported the results of KEYNOTE-028, a phase Ib study on PD-L1(+) patients with advanced solid tumors (<xref ref-type="bibr" rid="B38">38</xref>). Pembrolizumab displayed controllable toxicity and persistent antitumor activity in these patients. Yubo Jiang et al. revealed the prognostic significance of tumor-infiltrating immune cells and PD-L1 expression in ESCC (<xref ref-type="bibr" rid="B39">39</xref>).</p>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>Esophageal cancer has a high degree of malignancy and poor prognosis. As a new therapeutic method, immunotherapy can significantly improve the prognosis of patients (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B41">41</xref>). The anti-PD-1/PD-L1 antibody is the most commonly used ICI. Therefore, it is important to build an in-depth understanding of publications in this field. In this study, a bibliometric analysis of anti-PD-1/PD-L1 immunotherapy for esophageal cancer from 2012 to 2021 was performed, presenting the research hotspots and trends.</p>
<p>
<xref ref-type="fig" rid="f2">
<bold>Figure 2</bold>
</xref> shows that the annual output maintained a rapid growth over the last decade. Literature published between 2012 and 2016 mostly focused on the expression and prognostic value of PD-1/PD-L1. In 2017, results of clinical trials of ICIs for esophageal cancer began to be published. From then on, the annual output increased rapidly, from 40 in 2017 to 216 in 2021. The annual growth rate also increased year by year.</p>
<p>In 2017, Toshihiko Doi (<xref ref-type="bibr" rid="B38">38</xref>) and Toshihiro Kudo (<xref ref-type="bibr" rid="B29">29</xref>) released their respective clinical trial results, which respectively demonstrated that Pembrolizumab and Nivolumab had certain anti-tumor effect in PD-L1(+) patients who failed second-line or back-like treatment. In 2018, Huang Jing et al. conducted a study on 30 patients with relapsed or metastatic advanced ESCC that showed chemoresistance previously (<xref ref-type="bibr" rid="B42">42</xref>). According to their results, the anti-PD-1drug SHR-1210 exhibited definite antitumor activity, with tolerable toxic and side effects. In 2019, the research data from multiple clinical trials were released, including KEYNOTE-180 (<xref ref-type="bibr" rid="B30">30</xref>), KEYNOTE-181 (<xref ref-type="bibr" rid="B43">43</xref>) and ATTRACTION-03 (<xref ref-type="bibr" rid="B28">28</xref>). As revealed by KEYNOTE-180 and KEYNOTE-181, Pembrolizumab had remarkable therapeutic effect and favorable safety on patients with PD-L1(+) advanced esophageal carcinoma, supporting the application of Pembrolizumab as the new second-line standard treatment for PD-L1(+) metastatic esophageal carcinoma. In 2019, Pembrolizumab was approved by the USA FDA to be used to treat relapsed, locally advanced or metastatic ESCC patients who had received first-line or later-line systemic treatment, with positive PD-L1 expression in tumor tissues (CPS&#x2265;10). In 2020, a breakthrough was made in the immunotherapy for esophageal carcinoma. The preliminary research results from KEYNOTE-590 demonstrated the satisfactory therapeutic effect and safety of Pembrolizumab combined with chemotherapy in the first-line treatment for advanced esophageal carcinoma (<xref ref-type="bibr" rid="B44">44</xref>). In the 2020 V5 version of NCCN guidelines, Pembrolizumab combined with platinum-based chemotherapeutic regimens was recommended in the first-line treatment of unresectable, locally advanced, locally relapsed or metastatic esophageal carcinoma with PD-L1 CPS&#x2265;10 and negative HER-2 expression. Additionally, CheckMate-577 ushered in the new chapter of adjuvant immunotherapy for esophageal carcinoma, which comprehensively evaluated the therapeutic effect of adjuvant nivolumab on patients with esophageal carcinoma and gastroesophageal junction carcinoma who did not achieve complete pathological remission after neoadjuvant radiochemotherapy (NRCT) (<xref ref-type="bibr" rid="B45">45</xref>). Clinical trials of neoadjuvant immunotherapy, such as NICE, KEEP-G 03, and PALACE-1, also reported the preliminary results. In March 2021, based on the KEYNOTE-590 research results, the USA FDA approved the use of Pembrolizumab combined with platinum-based chemotherapy as the first-line treatment for unresectable locally advanced or metastatic esophageal carcinoma or gastrointestinal junction carcinoma or those not suitable for radical radiochemotherapy, regardless of the PD-L1 expression status. The results of neoadjuvant immunotherapy combined with chemotherapy or radiochemotherapy were also released in 2021. The rapid development of immunotherapy for esophageal cancer suggests the great potential of the field in the future. Given that the feasibility and safety of anti-PD-1/PD-L1 immunotherapy have been confirmed, there might be more publications in the following years. The development prospects of immunotherapy for esophageal cancer could be expected.</p>
<p>China was the top 1 country ranked by total publications, which was consistent with epidemiological status that the incidence rate and fatality rate were high in this country. Although China had a huge number of publications, its citation was not impressive. Germany and France with less publications had high ratio of Citations/Paper, reflecting the high quality of their publications. The USA was the most active collaborator in <xref ref-type="fig" rid="f3">
<bold>Figure 3</bold>
</xref> and played an important role in international cooperation.</p>
<p>As China is one of the high-risk areas of esophageal cancer, 6 of the top 10 institutions are from China. However, the articles published in China were scarcely cited, reflecting the weak influence of these publications. Therefore, Chinese institutions need to find methods to improve the quality of publications.</p>
<p>Natl Canc Ctr and Natl Canc Ctr Hosp East located in Japan were institutions that not only productive but also influential. They were both participating institutions of several important clinical trials, including KEYNOTE-180, KEYNOTE-181 and KEYNOTE-590. Ken Kato from National Cancer Center Hospital together with Toshihiko Doi and Takashi Kojima from National Cancer Center Hospital East were all contributed to the three clinical trials. They were also the top 3 prolific authors. Kato Ken who was the most prolific author ranked second in terms of co-cited authors, and he was a key figure of ATTRACTION-3. The top 10 prolific authors were all from Japan, indicating that Japanese scientists made a tremendous contribution in this field.</p>
<p>The analysis of prolific journals guides scientists in identifying core journals for information access and manuscript submissions. Three journals were highly recommended to scientists in the field: <italic>Frontiers in Oncology</italic>, <italic>Cancer Science</italic> and <italic>Journal for Immunotherapy of Cancer</italic>. Moreover, <italic>Journal of Clinical Oncology</italic>, <italic>Lancet Oncology</italic>, and <italic>New England Journal of Medicine</italic>, were the most authoritative journals in this field according to the co-citation amount shown in <xref ref-type="table" rid="T3">
<bold>Table 3</bold>
</xref>. As regard the subject categories shown in <xref ref-type="fig" rid="f6">
<bold>Figure 6</bold>
</xref> and <xref ref-type="table" rid="T5">
<bold>Table 5</bold>
</xref>, ONCOLOGY and IMMUNOLOGY occupied central positions in this field, which were consistent with the analyzing results of the journals.</p>
<p>The current research hotspots were obtained from the high frequency keywords and cited references, which helped researchers to rapidly understand the direction of the research. This work lists the remarkable highlights of the research field as follows.</p>
<p>Clinical significance had the highest burst strength among the 34 keywords. It has always been significant in the field from 2014 to 2018. During this period of time, many studies focused on the prognostic value of PD-1/PD-L1 in patients with esophageal cancer. Numerous studies have suggested that, PD-L1 expression is related to the adverse clinical outcomes of esophageal cancer, supporting its role as a prognostic biomarker (<xref ref-type="bibr" rid="B39">39</xref>, <xref ref-type="bibr" rid="B46">46</xref>&#x2013;<xref ref-type="bibr" rid="B49">49</xref>). Further study found that PD-L1 expression in ESCC tumor cells was significantly associated with worse survival while no statistical significance was found between PD-L1 expression in ESCC tumor-infiltrating immune cells and survival (<xref ref-type="bibr" rid="B50">50</xref>). Recently, Peipei Wang et al. claimed that increased co-expression of PD-L1 and TIM3/TIGIT was associated with poor overall survival of ESCC (<xref ref-type="bibr" rid="B51">51</xref>).</p>
<p>Neoadjuvant chemotherapy was the hottest keywords in the last two years. With the moving forward of immunotherapy, more and more publications about NCT or NRCT plus immunotherapy are available at present. Multiple studies have evaluated the safety, feasibility and efficacy of neoadjuvant PD-1/PD-L1 inhibitors combined with chemotherapy in treating esophageal cancer patients (<xref ref-type="bibr" rid="B52">52</xref>&#x2013;<xref ref-type="bibr" rid="B62">62</xref>). The neoadjuvant treatment of PD-1/PD-L1 inhibitor with chemotherapy produced satisfactory outcomes, indicating its potential as a promising neoadjuvant treatment for esophageal cancer. Besides, Wenqun Xing et al. designed a study to explore the impact of chemotherapy and toripalimab sequence on the pathological complete response (pCR) rate and safety of locally advanced ESCC patients (<xref ref-type="bibr" rid="B63">63</xref>). The initial results showed that delaying toripalimab to day 3 in chemoimmunotherapy might achieve a higher pCR rate than that on the same day. In the PERFECT trial, we investigated the feasibility and efficacy of NCRT combined with PD-L1 inhibitor (<xref ref-type="bibr" rid="B64">64</xref>). However, most of these studies were single-arm, phase I or II clinical trials. The long-term efficiency of this novel treatment and the validity of the present findings should be confirmed with more large-scale, longer follow-up and prospective comparative trials. In the existing clinical trials on neoadjuvant immunotherapy for esophageal carcinoma, no definite molecular biomarkers are available for selecting the possibly beneficial population. The previous research mainly focused on PD-L1, TMB, EGFR and CD8+ T cells. These studies not only help to identify the molecular biomarkers, but also provide ideas for the design of phase III clinical trials. Further studies should confirm more predictive biomarkers as well as indicators for the selection of a specific treatment.</p>
<p>The application of PD-1/PD-L1 inhibitors in esophageal cancer has achieved unprecedented successes. However, some treated patients exhibit non-response and severe immune-related adverse events. Therefore, immunotherapeutic markers are needed to assist in screening populations who can gain benefits from immunotherapy. At present, PD-L1 expression is used as the major biomarker for efficacy prediction in the application of PD-L1 inhibitors (<xref ref-type="bibr" rid="B65">65</xref>). With the deepening of research, DNA mismatch repair-deficient/microsatellite instability (dMMR/MSI) (<xref ref-type="bibr" rid="B66">66</xref>), tumor mutational burden (TMB) (<xref ref-type="bibr" rid="B67">67</xref>), copy number variation (CNV), polymerase epsilon (POLE) (<xref ref-type="bibr" rid="B68">68</xref>, <xref ref-type="bibr" rid="B69">69</xref>), circulating tumor DNA (ctDNA) (<xref ref-type="bibr" rid="B70">70</xref>), inflamed gene expression profile (<xref ref-type="bibr" rid="B71">71</xref>, <xref ref-type="bibr" rid="B72">72</xref>), tumor-infiltrating lymphocytes (TILs) (<xref ref-type="bibr" rid="B73">73</xref>), and immune gene signatures (<xref ref-type="bibr" rid="B74">74</xref>) have been suggested to show certain potential in predicting efficacy, which deserve further verification. Park R et al. Mentioned that, for esophageal cancer, there is no highly sensitive or specific marker apart from dMMR/MSI-H (<xref ref-type="bibr" rid="B75">75</xref>). Consequently, it is necessary to develop biomarkers for immunotherapy.</p>
<p>For the time being, the sensitivity and specificity of single biomarkers are not high enough. As a result, they may not be used as biomarkers alone. The combination of multiple biomarkers contributes more to predicting the immunotherapeutic efficacy in esophageal cancer. Moreover, when immunotherapy is used in combination with other treatments, whether a specific biomarker can maintain its prediction ability should be further analyzed. Whether predictors verified in advanced tumors can be applied in perioperative treatment is another important problem to be answered by on-going and future trials. In the future, it is promising to develop more precise tools to predict the anti-PD-1/PD-L1 therapeutic efficacy in esophageal cancer patients by standardizing and normalizing diverse biomarkers, intensively investigating the relations of different biomarkers, and applying computer technologies and medical databases, which is of great significance for individualized immunotherapy.</p>
<p>As far as we know, this is the first bibliometric study regarding anti-PD-1/PD-L1 immunotherapy for esophageal cancer in the past decade. The article hopes to guide scholars select research direction, references, cooperative institutions and authoritative journals. The data analysis was relatively objective and comprehensive, clearly displaying the research status visually. However, here are some limitations as follows.</p>
<list list-type="order">
<list-item>
<p>The study included articles and reviews retrieved from WoSCC. Articles of other types or from other databases could not be involved in our study, thus limit the comprehensiveness of the study.</p>
</list-item>
<list-item>
<p>Papers published from 2012 to 2021 were retrieved on June 20, 2022. However, the database is still updating the data. Therefore, some recent publications could not be included. Besides, the number of citations of recent literature might be affected.</p>
</list-item>
<list-item>
<p>All of the publications included were in English, which might lead to a linguistic bias. Languages like Chinese, Japanese, French, German, Polish, Hungarian, Portuguese, Rumanian and Korean were not involved in the database. Therefore, it is likely that our results may not be applicable to publications in other languages.</p>
</list-item>
<list-item>
<p>Although analysis process was performed by software objectively, the method to explain these results had inherent subjective bias by individuals.</p>
</list-item>
</list>
<p>Still, it is believed that this article provides the overall situation and research trend of anti-PD-1/PD-L1 immunotherapy for esophageal cancer. The results could provide readers a general overview of the landscape, especially to those without in-depth knowledge. The information could also be used to explore possible collaboration partners, potentially relevant publications, and promising research directions. Our study not only exhibits important milestones of esophageal cancer immunotherapy but also offers a better guide to the future. We sincerely hope that bibliometric and visual analyses will give us more ideas in this field.</p>
</sec>
<sec id="s5">
<title>Conclusion</title>
<p>To conclude, this article provides a comprehensive understanding of publications on anti-PD-1/PD-L1 immunotherapy for esophageal cancer from 2012 to 2021, providing valuable information to researchers in this field. This article presents data on the trend of annual output, countries/regions, institutions, journals, authors, subject categories, keywords, and co-cited references obtained using bibliometric analysis. Neoadjuvant chemotherapy, response, PD-1 blockade and CD8+ T cell were four latest research frontiers. Further studies and more cooperation are needed worldwide. Overall, our results could help the discovery of new perspectives and determine future directions.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>Data were retrieved from 552 publications with VOSviewer on June 20, 2022. 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/ supplementary material.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>Study conception, design and data analysis: YY. Paper writing: YY. Language polishing, paper review and editing: FW. All authors read and approved the submitted version. 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 study was supported by Health Training Program Foundation for Young and Middle-Aged Innovative Talents of Science and Technology (YXKC2020017) and Programs for Medical Science and Technology Development of Henan Province of China (SBGJ202002080).</p>
</sec>
<sec id="s9" sec-type="acknowledgement">
<title>Acknowledgments</title>
<p>We thank Danyang Chen for assistance with manuscript preparation.</p>
</sec>
<sec id="s10" 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="s11" 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>
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