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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.2023.1232409</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Systematic Review</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Prognostic significance of C-reactive protein in patients with cervical cancer: a meta-analysis</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Sheng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Zongxin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Shen</surname>
<given-names>Linglong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2331560"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Clinical Laboratory, Huzhou Maternity and Child Health Care Hospital</institution>, <addr-line>Huzhou, Zhejiang</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University</institution>, <addr-line>Huzhou, Zhejiang</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Francesco Fanfani, Agostino Gemelli University Polyclinic (IRCCS), Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Francesco Ricchetti, Sacro Cuore Don Calabria Hospital (IRCCS), Italy; Nanfang Peng, Memorial Sloan Kettering Cancer Center, United States</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Linglong Shen, <email xlink:href="mailto:chaoren5121132023@163.com">chaoren5121132023@163.com</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>01</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>13</volume>
<elocation-id>1232409</elocation-id>
<history>
<date date-type="received">
<day>31</day>
<month>05</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>08</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Yang, Zhang and Shen</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Yang, Zhang and Shen</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Numerous studies have investigated the significance of pretreatment C-reactive protein (CRP) levels for determining the prognosis of cervical cancer (CC). The results of these studies, however, have been inconsistent. The present meta-analysis, therefore, focused on identifying the exact relationship of CRP levels with CC prognoses.</p>
</sec>
<sec>
<title>Methods</title>
<p>We searched the following databases from their inception until April 18, 2023: PubMed; Web of Science; Embase; and Cochrane Library. From the search results, we estimated the significance of CRP levels in determining the prognosis of CC, based on combined hazard ratios (HRs) and relevant 95% confidence intervals (CIs).</p>
</sec>
<sec>
<title>Results</title>
<p>The present meta-analysis included 12 studies, encompassing 2,204 patients. Based on combined data, an increased CRP level was significantly related to an unfavorable overall survival (OS) of patients with CC (HR = 1.63; 95% CI = 1.36&#x2013;1.95; <italic>P</italic> &lt; 0.001). Moreover, an increased CRP level was significantly associated with shortened progression-free survival (PFS) in patients with CC (HR = 1.68; 95% CI = 1.39&#x2013;2.03; <italic>P</italic> &lt; 0.001). According to the subgroup and sensitivity analyses, CRP level was a reliable factor in determining CC prognoses.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Based on the results of our present analyses, increased CRP levels were significant predictors of poor OS and PFS in patients with CC. CRP level, therefore, could be an independent and inexpensive factor for determining the prognosis of patients with CC in clinical settings.</p>
</sec>
<sec>
<title>Systematic review registration</title>
<p>INPLASY, identifier INPLASY202360074.</p>
</sec>
</abstract>
<kwd-group>
<kwd>C-reactive protein</kwd>
<kwd>cervical cancer</kwd>
<kwd>meta-analysis</kwd>
<kwd>prognosis</kwd>
<kwd>evidence-based medicine</kwd>
</kwd-group>
<counts>
<fig-count count="5"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="52"/>
<page-count count="11"/>
<word-count count="3860"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Gynecological Oncology</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Cervical cancer (CC) ranks the fourth most common cancer in women; besides, it is also the fourth frequent cause of cancer death among women globally (<xref ref-type="bibr" rid="B1">1</xref>). There were 604,127 new cases of CC and 6 341,831 deaths due to CC in 2020 worldwide (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). The primary etiological factor for CC is chronic human papillomavirus (HPV) infection (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B4">4</xref>), and surgical resection is the preferred treatment for patients diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stages &#x2264; IIA, while chemoradiation is recommended for patients diagnosed with higher stages (<xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B5">5</xref>). It is estimated, however, that 20&#x2013;25% of patients with CC will experience a recurrence after completing their primary treatment, and that locally advanced CC has a poor 5-year survival rate (<xref ref-type="bibr" rid="B6">6</xref>). To some extent, the lack of efficient prognostic markers may have a correlation to poor CC survival. Therefore, establishing reliable and easily available biomarkers for determining the prognosis of patients with CC would aid the development of treatment strategies.</p>
<p>A growing body of evidence suggests that inflammation is involved in the pathogenesis and development of solid tumors (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B8">8</xref>). When infection, tissue injury, trauma, neoplastic growth, or surgery interferes with the homeostasis of an organism, an acute-phase response (APR) is immediately triggered (<xref ref-type="bibr" rid="B9">9</xref>). C-reactive protein (CRP), a sensitive and well-recognized systemic inflammatory marker produced by the liver in response to factors such as interleukin (IL)-6, IL-1, along with tumor necrosis factor-&#x3b1; (TNF-&#x3b1;) (<xref ref-type="bibr" rid="B10">10</xref>). CRP is an acute phase reactant that is widely considered to be a marker of both acute and chronic inflammation (<xref ref-type="bibr" rid="B11">11</xref>). CRP has several well-defined functions, including acting as a pattern recognition receptor with calcium-sensitive binding pockets for ligands expressing phosphocholine (PC) moieties (<xref ref-type="bibr" rid="B12">12</xref>). CRP can also bind to activated cell membranes in which the PC groups on phospholipids become accessible when diacylphospholipids are hydrolyzed into monoacylphospholipids (<xref ref-type="bibr" rid="B12">12</xref>). In addition to being found in serum, CRP has recently been found to exist in an array of cyclic pentameric discs. The effects of CRP on the behavior of cells and the microenvironment are, therefore, dependent on its structural configuration. Pentameric CRP (pCRP) binds to PC on the surface of the cell, causing it to dissociate into its distinct monomeric isoform (mCRP), which subsequently reduces its aqueous solubility (<xref ref-type="bibr" rid="B13">13</xref>). It has been suggested that CRP levels are an effective factor in determining the prognosis of various cancers, including esophageal cancer (<xref ref-type="bibr" rid="B14">14</xref>), renal cell carcinoma (RCC) (<xref ref-type="bibr" rid="B15">15</xref>), prostate cancer (<xref ref-type="bibr" rid="B16">16</xref>), and gastric cancer (<xref ref-type="bibr" rid="B17">17</xref>). The relationship between CRP levels and survival outcomes in patients with CC has been extensively investigated; however, no consistent results have been reported thus far (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B29">29</xref>). Elevated CRP levels have been reported to be a significant prognostic factor for CC in some studies (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>), while others have demonstrated a non-significant association between CRP levels and CC prognosis (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B24">24</xref>). Given these inconsistent results, we aimed to comprehensively search relevant articles in the present meta-analysis to quantitatively identify the true relationship of CRP levels and CC prognoses.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Study guidelines</title>
<p>The present meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (<xref ref-type="bibr" rid="B30">30</xref>), and was registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) platform. The registration number of this meta-analysis on INPLASY is INPLASY202360074.</p>
</sec>
<sec id="s2_2">
<title>Ethnics statement</title>
<p>The present meta-analysis did not require the involvement of an institutional review board or ethics committee, as all of the data acquired were derived from previously published studies.</p>
</sec>
<sec id="s2_3">
<title>Literature search</title>
<p>In the present study, we thoroughly searched the PubMed, Web of Science, Embase, and Cochrane Library databases from their inception until April 18, 2023, for the following keywords/search terms: (C-reactive protein OR CRP OR c-reactive protein) AND (cervical cancer OR cervical carcinoma OR uterine cervix cancer OR cervical neoplasm OR cervix cancer). Only English studies were considered. After the initial search, the references from each of the included articles were manually searched to identify additional relevant articles.</p>
</sec>
<sec id="s2_4">
<title>Eligibility criteria</title>
<p>The inclusion criteria were as follows: (i) CC diagnosis confirmed based on pathology; (ii) studies explored the between serum CRP levels and any survival outcomes of patients with CC; (iii) available hazard ratios (HRs) and associated 95% confidence intervals (CIs) for prognoses from studies or calculable data based on the information provided in the articles; (iv) a cut-off value was defined with which to determine low/high CRP levels; and (v) studies were published in English. The exclusion criteria were as follows: (i) articles formatted as reviews, meeting abstracts, case reports, comments, or letters; (ii) patients with new infections, or chronic infection diseases, autoimmune diseases, organ dysfunctions, hematologic diseases, and patients with another type of tumor; (iii) articles involved animal studies; and (iv) duplicate articles.</p>
</sec>
<sec id="s2_5">
<title>Extraction of data and evaluation of study quality</title>
<p>Two researchers (SY and ZZ) independently screened all eligible studies and extracted the information required from each, which included the following: first author; publication year; country; sample size; study duration; age; FIGO stage; treatment; CRP level cut-off; cut-off determination method; survival endpoints; follow-up; survival analysis type; and HRs with 95% CIs. All disagreements were resolved by reaching a consensus after discussion with a third reviewer (LS). The overall survival (OS) was selected as the primary outcome, and progression-free survival (PFS) as the secondary outcome. Additionally, we utilized the Newcastle-Ottawa Scale (NOS) score to evaluate the quality of the included articles (<xref ref-type="bibr" rid="B31">31</xref>). The NOS evaluates studies based on 3 factors cohort selection; comparability; and results. A NOS score &#x2265; 6, based on a 0&#x2013;9 point scale, suggests high-quality research.</p>
</sec>
<sec id="s2_6">
<title>Statistical analysis</title>
<p>Pooled HRs and 95% CIs were calculated to estimate the significance of CRP levels in determining the prognosis for patients with CC. In general, a combined HR &gt; 1 with a 95% CI not overlapping 1 indicated a significant association with poor prognosis, while a combined HR &lt; 1 with a 95% CI not overlapping 1 indicated a better prognosis. Inter-study heterogeneity was evaluated using Cochran&#x2019;s Q-test (<xref ref-type="bibr" rid="B32">32</xref>) and I<sup>2</sup> statistics (<xref ref-type="bibr" rid="B33">33</xref>). I<sup>2</sup> was used to quantify the degree of heterogeneity among the studies, as follows: I<sup>2</sup> &lt; 25%, low degree; 25&#x2013;75%, moderate degree; and &gt; 75%, high degree of heterogeneity (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>). To analyze the pooled data, we used two different computational models, based on the traits of the included studies, and the cut-off for significant heterogeneity was set at I<sup>2</sup> &gt; 50% (<xref ref-type="bibr" rid="B35">35</xref>&#x2013;<xref ref-type="bibr" rid="B38">38</xref>). When high heterogeneity was determined based on I<sup>2</sup> &gt; 50% and Q-test P &lt; 0.10, and a random-effects model (REM; DerSimonian-Laird method) was used (<xref ref-type="bibr" rid="B39">39</xref>); otherwise, a fixed-effects model (FEM; Mantel-Haenszel method) was used (<xref ref-type="bibr" rid="B40">40</xref>). Subgroup analyses of OS and PFS were conducted to identify possible sources of heterogeneity, and we conducted a sensitivity analysis by removing one article at a time, in order, to evaluate the robustness of the combined results. Funnel plots and Begg&#x2019;s test (<xref ref-type="bibr" rid="B41">41</xref>) were utilized to evaluate publication bias. All statistical analyses were conducted using Stata software version 12.0 (Stata Corporation, College Station, TX, USA), with P &lt; 0.05 indicating a significant difference.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Included literature</title>
<p>During the initial literature search, 850 relevant articles were identified, and after the removal of duplicate studies, 692 studies remained (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). After title and abstract screening, 658 articles were excluded because they were irrelevant studies or animal studies, the full texts of the remaining 34 articles were read, and 22 articles were further eliminated, for the following reasons: irrelevance to CRP (n = 10); non-available survival analysis data (n = 9); irrelevance to CC (n = 1); no identified cut-off value (n = 1); or overlapping cases (n = 1). In total, we included 12 articles in the present study, encompassing 2,204 cases (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>, <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>) (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B29">29</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>PRISMA flowchart of the established screening strategy.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1232409-g001.tif"/>
</fig>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Basic characteristics of included studies in this meta-analysis.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="left">Author</th>
<th valign="top" align="left">Year</th>
<th valign="top" align="left">Country</th>
<th valign="top" align="left">Sample size</th>
<th valign="top" align="left">Study period</th>
<th valign="top" align="left">Age (year)<break/>Median(range)</th>
<th valign="top" align="left">FIGO stage</th>
<th valign="top" align="left">Treatment</th>
<th valign="top" align="left">CRP cut-off value (mg/L)</th>
<th valign="top" align="left">Cut-off determination</th>
<th valign="top" align="left">Survival outcome</th>
<th valign="top" align="left">Follow-up<break/>(month)<break/>Median(range)</th>
<th valign="top" align="left">Survival analysis</th>
<th valign="top" align="left">NOS score</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Polterauer, S.</td>
<td valign="top" align="left">2007</td>
<td valign="top" align="left">Austria</td>
<td valign="top" align="left">214</td>
<td valign="top" align="left">1995-2006</td>
<td valign="top" align="left">50</td>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">29.4</td>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">7</td>
</tr>
<tr>
<td valign="top" align="left">Nakamura, K.</td>
<td valign="top" align="left">2015</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">32</td>
<td valign="top" align="left">2005-2014</td>
<td valign="top" align="left">52.6 (25&#x2013;78)</td>
<td valign="top" align="left">Recurrent</td>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">6.6(1.4-34.1)</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">7</td>
</tr>
<tr>
<td valign="top" align="left">Xiao, Y.</td>
<td valign="top" align="left">2015</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">238</td>
<td valign="top" align="left">2004-2011</td>
<td valign="top" align="left">54(34-70)</td>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">10</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">42(15-91)</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">Bodner-Adler, B.</td>
<td valign="top" align="left">2016</td>
<td valign="top" align="left">Austria</td>
<td valign="top" align="left">46</td>
<td valign="top" align="left">2005-2015</td>
<td valign="top" align="left">51(24-60)</td>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">1-167</td>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">7</td>
</tr>
<tr>
<td valign="top" align="left">He, X.</td>
<td valign="top" align="left">2018</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">229</td>
<td valign="top" align="left">2007-2009</td>
<td valign="top" align="left">44(28-79)</td>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">10</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">1-80</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">Wang, W. J.</td>
<td valign="top" align="left">2019</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">110</td>
<td valign="top" align="left">2012-2014</td>
<td valign="top" align="left">51.5(25-70)</td>
<td valign="top" align="left">I-II</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">2.62</td>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">1-36</td>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">An, Q.</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">278</td>
<td valign="top" align="left">2010-2017</td>
<td valign="top" align="left">45.5</td>
<td valign="top" align="left">I-II</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">6.1</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">1-60</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">7</td>
</tr>
<tr>
<td valign="top" align="left">Wang, H.</td>
<td valign="top" align="left">2020</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">150</td>
<td valign="top" align="left">2013-2015</td>
<td valign="top" align="left">59(24-75)</td>
<td valign="top" align="left">I-III</td>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">39(8-77)</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">Bakir, M. S.</td>
<td valign="top" align="left">2021</td>
<td valign="top" align="left">Turkey</td>
<td valign="top" align="left">243</td>
<td valign="top" align="left">2002-2020</td>
<td valign="top" align="left">48(28-84)</td>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">9.59</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">70.2(0.5-211.2)</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">Taguchi, A.</td>
<td valign="top" align="left">2021</td>
<td valign="top" align="left">Japan</td>
<td valign="top" align="left">89</td>
<td valign="top" align="left">2004-2015</td>
<td valign="top" align="left">67</td>
<td valign="top" align="left">Recurrent</td>
<td valign="top" align="left">Radiotherapy</td>
<td valign="top" align="left">7.35</td>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">OS</td>
<td valign="top" align="left">16.4</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">7</td>
</tr>
<tr>
<td valign="top" align="left">Li, Y.</td>
<td valign="top" align="left">2022</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">460</td>
<td valign="top" align="left">2011-2019</td>
<td valign="top" align="left">49.8</td>
<td valign="top" align="left">I-III</td>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">2.6</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">OS, PFS</td>
<td valign="top" align="left">1-90</td>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
</tr>
<tr>
<td valign="top" align="left">Zheng, X.</td>
<td valign="top" align="left">2023</td>
<td valign="top" align="left">China</td>
<td valign="top" align="left">115</td>
<td valign="top" align="left">2020-2022</td>
<td valign="top" align="left">54(32-70)</td>
<td valign="top" align="left">Recurrent/<break/>metastatic</td>
<td valign="top" align="left">Anti-PD-1</td>
<td valign="top" align="left">3.08</td>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">PFS</td>
<td valign="top" align="left">11.3(2.2-28.7)</td>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">9</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>CRP, C-reactive protein; CCRT, concurrent chemoradiotherapy; ROC, receiver operating characteristic; OS, overall survival; PFS, progression-free survival; FIGO, International Federation of Gynecology and Obstetrics; NOS, Newcastle-Ottawa Scale; PD-1, programmed death-1.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Study features</title>
<p>
<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref> summarizes the basic characteristics of the present study. All of the eligible studies were published between 2007 and 2023, with 7 published in China (<xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>&#x2013;<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>), 2 in Austria (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B21">21</xref>), 2 in Japan (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B27">27</xref>), and 1 in Turkey (<xref ref-type="bibr" rid="B26">26</xref>). Sample sizes ranged from 32 to 460 (median, 182) participants. Of the 12 included studies, 5 recruited patients diagnosed with FIGO stages I&#x2013;IV (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B20">20</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B26">26</xref>), 2 with stages I&#x2013;II (<xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>), and 2 with stages I&#x2013;III (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B28">28</xref>), while 3 included recurrent/metastatic tumor cases (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B27">27</xref>, <xref ref-type="bibr" rid="B29">29</xref>). The threshold CRP value was 2.6&#x2013;10 (median, 5.55) mg/L, and 8 articles determined the cut-off value using receiver operating characteristic (ROC) analysis (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>), while 4 studies utilized the median value of that individual study (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B27">27</xref>). Of the 12 articles, 11 mentioned the significance of CRP in predicting OS (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B28">28</xref>), and 6 investigated the association between CRP and PFS in patients with CC (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>). HRs, together with the relevant 95% CIs, were collected through univariate regression in 9 articles (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B22">22</xref>, <xref ref-type="bibr" rid="B24">24</xref>&#x2013;<xref ref-type="bibr" rid="B28">28</xref>) and multivariate analysis in 4 (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B29">29</xref>). The NOS scores all ranged from 7&#x2013;9 (median, 8), indicating high quality articles (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>).</p>
</sec>
<sec id="s3_3">
<title>Significance of CRP in predicting OS</title>
<p>A total of 11 articles, encompassing 2,089 patients (<xref ref-type="bibr" rid="B18">18</xref>&#x2013;<xref ref-type="bibr" rid="B28">28</xref>), presented data on the relationship between the CRP value and OS in patients with CC. REM was utilized in these articles because significant heterogeneity was detected (I<sup>2&#xa0;=&#xa0;</sup>71.6%; P &lt; 0.001). In <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref> and <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>, as seen based on the pooled data, increased CRP expression was significantly tied to extremely poor OS in patients with CC (HR = 1.63; 95% CI = 1.36&#x2013;1.95; <italic>P</italic> &lt; 0.001). Subgroup analyses were conducted based on various factors. As seen in <xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>, subgroup analysis results indicated that elevated levels of serum CRP were still a significant marker for predicting OS in patients with CC regardless of sample size, country, FIGO stage, treatment, CRP cut-off, cut-off determination, or survival analysis type (all, <italic>P</italic> &lt; 0.05).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Subgroup analysis of prognostic role of CRP for OS in patients with CC.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="left">Subgroups</th>
<th valign="top" rowspan="2" align="left">No. of studies</th>
<th valign="top" rowspan="2" align="left">No. of patients</th>
<th valign="top" rowspan="2" align="left">Effects model</th>
<th valign="top" rowspan="2" align="left">HR (95%CI)</th>
<th valign="top" rowspan="2" align="left">p</th>
<th valign="top" colspan="2" align="left">Heterogeneity</th>
</tr>
<tr>
<th valign="top" align="left">I<sup>2</sup>(%)</th>
<th valign="top" align="left">Ph</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Total</td>
<td valign="top" align="left">11</td>
<td valign="top" align="left">2,089</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.63(1.36-1.95)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">71.6</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Country</th>
</tr>
<tr>
<td valign="top" align="left">China</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">1,465</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.57(1.32-1.86)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">45.9</td>
<td valign="top" align="left">0.100</td>
</tr>
<tr>
<td valign="top" align="left">Non-China</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">624</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.61(1.24-2.10)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">80.7</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Sample size</th>
</tr>
<tr>
<td valign="top" align="left">&lt;200</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">427</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.25(1.17-1.33)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">42.1</td>
<td valign="top" align="left">0.141</td>
</tr>
<tr>
<td valign="top" align="left">&#x2265;200</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">1,662</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.92(1.36-2.71)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">71.6</td>
<td valign="top" align="left">0.003</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">FIGO stage</th>
</tr>
<tr>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">970</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">2.14(1.38-3.31)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">82.8</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="top" align="left">I-II/I-III</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">998</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.42(1.16-1.74)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">35.2</td>
<td valign="top" align="left">0.201</td>
</tr>
<tr>
<td valign="top" align="left">Recurrent/<break/>metastatic</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">121</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.23(1.15-1.32)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.446</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Treatment</th>
</tr>
<tr>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">880</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.56(1.27-1.92)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">35.2</td>
<td valign="top" align="left">0.201</td>
</tr>
<tr>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">1,120</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.94(1.33-2.83)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">78.3</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
<tr>
<td valign="top" align="left">Radiotherapy</td>
<td valign="top" align="left">1</td>
<td valign="top" align="left">89</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">1.23(1.15-1.32)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Cut-off value</th>
</tr>
<tr>
<td valign="top" align="left">&#x2264;5 mg/L</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">980</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.60(1.20-2.13)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">65.7</td>
<td valign="top" align="left">0.020</td>
</tr>
<tr>
<td valign="top" align="left">&gt;5 mg/L</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">1,109</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.77(1.26-2.49)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">78.4</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Cut-off determination</th>
</tr>
<tr>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">7</td>
<td valign="top" align="left">1,526</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.64(1.25-2.14)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">70.3</td>
<td valign="top" align="left">0.003</td>
</tr>
<tr>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">563</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.83(1.18-2.83)</td>
<td valign="top" align="left">0.007</td>
<td valign="top" align="left">76.9</td>
<td valign="top" align="left">0.005</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Survival analysis</th>
</tr>
<tr>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">3</td>
<td valign="top" align="left">370</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.80(1.03-3.14)</td>
<td valign="top" align="left">0.037</td>
<td valign="top" align="left">75.9</td>
<td valign="top" align="left">0.016</td>
</tr>
<tr>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">8</td>
<td valign="top" align="left">1,719</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.67(1.30-2.14)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">73.9</td>
<td valign="top" align="left">&lt;0.001</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>REM, random-effects model; FEM, fixed-effects model.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Forest plots of the prognostic role of CRP for OS in patients with CC.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1232409-g002.tif"/>
</fig>
</sec>
<sec id="s3_4">
<title>Prognostic value of CRP for PFS</title>
<p>Of the 12 included articles, 6 studies, encompassing 1,415 patients (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B20">20</xref>, <xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B28">28</xref>, <xref ref-type="bibr" rid="B29">29</xref>), provided data on the role of CRP values in predicting the PFS of patients with CC. The FEM was utilized with these articles, due to their low level of heterogeneity (I<sup>2&#xa0;=&#xa0;</sup>22.9%; <italic>P</italic> = 0.262; <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>). In line with our aforementioned analysis, however, increased CRP levels were significantly correlated with shortened PFS in patients with CC (HR = 1.68; 95% CI = 1.39&#x2013;2.03; <italic>P</italic> &lt; 0.001; <xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>, <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>). Similar to what we observed with the subgroup analysis for OS, an increased CRP level was significant in predicting PFS regardless of sample size, country, FIGO stage, treatment, CRP cut-off, cut-off determination, or survival analysis type (<italic>P</italic> &lt; 0.05; <xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>Forest plots of the prognostic role of CRP for PFS in patients with CC.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1232409-g003.tif"/>
</fig>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>Subgroup analysis of prognostic role of CRP for PFS in patients with CC.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" rowspan="2" align="left">Subgroups</th>
<th valign="top" rowspan="2" align="center">No. of studies</th>
<th valign="top" rowspan="2" align="center">No. of patients</th>
<th valign="top" rowspan="2" align="center">Effects model</th>
<th valign="top" rowspan="2" align="center">HR (95%CI)</th>
<th valign="top" rowspan="2" align="center">p</th>
<th valign="top" colspan="2" align="center">Heterogeneity<break/>I<sup>2</sup>(%) Ph</th>
</tr>
<tr>
<th valign="top" align="center">I<sup>2</sup>(%)</th>
<th valign="top" align="center">Ph</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Total</td>
<td valign="top" align="left">6</td>
<td valign="top" align="left">1,415</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.68(1.39-2.03)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">22.9</td>
<td valign="top" align="left">0.262</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Country</th>
</tr>
<tr>
<td valign="top" align="left">China</td>
<td valign="top" align="left">5</td>
<td valign="top" align="left">1,201</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.64(1.34-2.01)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">32.3</td>
<td valign="top" align="left">0.206</td>
</tr>
<tr>
<td valign="top" align="left">Non-China</td>
<td valign="top" align="left">1</td>
<td valign="top" align="left">214</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">2.10(1.15-3.83)</td>
<td valign="top" align="left">0.016</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Sample size</th>
</tr>
<tr>
<td valign="top" align="left">&lt;200</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">225</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">2.18(1.17-4.04)</td>
<td valign="top" align="left">0.014</td>
<td valign="top" align="left">51.9</td>
<td valign="top" align="left">0.149</td>
</tr>
<tr>
<td valign="top" align="left">&#x2265;200</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">1,190</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.59(1.28-1.98)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">8.9</td>
<td valign="top" align="left">0.349</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">FIGO stage</th>
</tr>
<tr>
<td valign="top" align="left">I-IV</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">452</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.93(1.43-2.62)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.756</td>
</tr>
<tr>
<td valign="top" align="left">I-II/I-III</td>
<td valign="top" align="left">3</td>
<td valign="top" align="left">848</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.41(1.09-1.82)</td>
<td valign="top" align="left">0.010</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.674</td>
</tr>
<tr>
<td valign="top" align="left">Recurrent/<break/>metastatic</td>
<td valign="top" align="left">1</td>
<td valign="top" align="left">115</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">3.20(1.53-6.68)</td>
<td valign="top" align="left">0.002</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Treatment</th>
</tr>
<tr>
<td valign="top" align="left">CCRT</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">698</td>
<td valign="top" align="left">REM</td>
<td valign="top" align="left">1.55(1.08-2.23)</td>
<td valign="top" align="left">0.017</td>
<td valign="top" align="left">55.6</td>
<td valign="top" align="left">0.134</td>
</tr>
<tr>
<td valign="top" align="left">Mixed</td>
<td valign="top" align="left">3</td>
<td valign="top" align="left">602</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.75(1.24-2.47)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.693</td>
</tr>
<tr>
<td valign="top" align="left">Anti-PD-1</td>
<td valign="top" align="left">1</td>
<td valign="top" align="left">115</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">3.20(1.53-6.68)</td>
<td valign="top" align="left">0.002</td>
<td valign="top" align="left">&#x2013;</td>
<td valign="top" align="left">&#x2013;</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Cut-off value</th>
</tr>
<tr>
<td valign="top" align="left">&#x2264;5 mg/L</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">899</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.64(1.29-2.05)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">48.0</td>
<td valign="top" align="left">0.123</td>
</tr>
<tr>
<td valign="top" align="left">&gt;5 mg/L</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">516</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.80(1.29-2.50)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.486</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Cut-off determination</th>
</tr>
<tr>
<td valign="top" align="left">ROC curve</td>
<td valign="top" align="left">4</td>
<td valign="top" align="left">1,091</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.63(1.31-2.04)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">49.1</td>
<td valign="top" align="left">0.117</td>
</tr>
<tr>
<td valign="top" align="left">Median value</td>
<td valign="top" align="left">2</td>
<td valign="top" align="left">324</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.83(1.26-2.66)</td>
<td valign="top" align="left">0.001</td>
<td valign="top" align="left">0</td>
<td valign="top" align="left">0.570</td>
</tr>
<tr>
<th valign="top" colspan="8" align="left">Survival analysis</th>
</tr>
<tr>
<td valign="top" align="left">Multivariate</td>
<td valign="top" align="left">3</td>
<td valign="top" align="left">439</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">2.05(1.47-2.86)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">4.2</td>
<td valign="top" align="left">0.352</td>
</tr>
<tr>
<td valign="top" align="left">Univariate</td>
<td valign="top" align="left">3</td>
<td valign="top" align="left">976</td>
<td valign="top" align="left">FEM</td>
<td valign="top" align="left">1.53(1.21-1.93)</td>
<td valign="top" align="left">&lt;0.001</td>
<td valign="top" align="left">14.9</td>
<td valign="top" align="left">0.309</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>REM, random-effects model; FEMl, fixed-effects model.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Sensitivity analysis</title>
<p>The impact of each article on the combined results was analyzed by performing a sensitivity analysis, which revealed stable outcomes for both OS (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4A</bold>
</xref>) and PFS (<xref ref-type="fig" rid="f4">
<bold>Figure&#xa0;4B</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>Sensitivity analysis. <bold>(A)</bold> OS; and <bold>(B)</bold> PFS.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1232409-g004.tif"/>
</fig>
</sec>
<sec id="s3_6">
<title>Publication bias</title>
<p>Begg&#x2019;s test and funnel plots were used to assess publication bias. As shown in <xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>, symmetry was observed in the funnel plots. For Begg&#x2019;s test, the resulting P-value for OS was 0.082, while that for PFS was 0.452, indicating the absence of significant publication bias in the present study.</p>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Publication test by Begg&#x2019; test. <bold>(A)</bold> Begg&#x2019;s test for OS, p=0.082; <bold>(B)</bold> Begg&#x2019;s test for PFS, p=0.452.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1232409-g005.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>Although previous studies have evaluated the significance of CRP levels in determining the prognoses of patients with CC, the results were inconsistent. The present meta-analysis, which included 12 articles encompassing 2,204 cases, aimed to definitively identify the relationship of CRP levels with CC prognoses. Based on the findings of the present meta-analysis, increased serum CRP levels are significantly predictive of OS and PFS in patients with CC, and as demonstrated by the subgroup and sensitivity analyses, this relationship was very reliable. Taken together, these results suggest that serum CRP level may be a useful prognostic biomarker for patients with CC. To the best of our knowledge, this is the first meta-analysis to explore the prognostic significance of serum CRP levels in patients with CC.</p>
<p>CRP is a well-established biomarker for inflammation and can be easily assayed in clinical practice (<xref ref-type="bibr" rid="B42">42</xref>). Additionally, as an acute-phase protein generated by the liver, CRP is indicative of whether inflammation exists, as well as its level in the body, while maintaining certain advantages, such as a stable half-life, cost-effectiveness, simple measurement, and standardization (<xref ref-type="bibr" rid="B43">43</xref>). There are multiple mechanisms underlying the prognostic value of CRP in patients with CC. First, the presence of tumor tissue causes inflammation, which, in turn, increases serum CRP levels (<xref ref-type="bibr" rid="B44">44</xref>); therefore, increased CRP levels indicate tumor necrosis or local tissue injury. Second, IL-6 can induce hepatocytes to produce CRP, as can other factors, such as IL-1, TNF-&#x3b1;, and transforming growth factor &#x3b2; (TGF&#x3b2;) (<xref ref-type="bibr" rid="B45">45</xref>), while via vascular endothelial growth factor (VEGF), IL-6 may promote angiogenesis in the presence of inflammation (<xref ref-type="bibr" rid="B46">46</xref>). Third, it has been shown that CRP induces an inflammatory tumor microenvironment that activates certain signaling pathways important for tumor proliferation, angiogenesis, and metastasis (<xref ref-type="bibr" rid="B47">47</xref>). It is likely, therefore, that patients with CC and a systemic inflammatory response, as indicated by elevated serum CRP levels, will be given a poor prognosis.</p>
<p>The present meta-analysis has several strengths. To the best of the authors&#x2019; knowledge, this is the first meta-analysis to explore the relationship between CRP levels and survival outcomes in patients with CC. Second, all included studies were of high quality (NOS score &#x2265; 6), which contributes to the overall statistical power of this meta-analysis. Third, sensitivity analyses and publication bias tests confirmed the reliability of the results of the present meta-analysis.</p>
<p>Numerous recent meta-analyses have reported the use of CRP in determining the prognoses of a variety of solid tumors. In a meta-analysis of 12 articles, Liu et&#xa0;al. (<xref ref-type="bibr" rid="B48">48</xref>) reported that serum CRP levels predicted poor OS in patients with prostate cancer. Li et&#xa0;al. (<xref ref-type="bibr" rid="B49">49</xref>) demonstrated that an increased CRP level indicated poor OS in patients with bone tumors, based on a meta-analysis of 816 participants. The results of a meta-analysis of 44 studies indicated that CRP levels could be used to predict OS and cancer-specific survival (CSS) in urological cancer cases (<xref ref-type="bibr" rid="B50">50</xref>). Jin et&#xa0;al. (<xref ref-type="bibr" rid="B51">51</xref>) carried out a meta-analysis encompassing 1,649 patients, the results of which showed that elevated CRP levels were indicative of poor OS in patients with non-small cell lung cancer, while Zheng et&#xa0;al. (<xref ref-type="bibr" rid="B52">52</xref>) reported that increased serum CRP levels indicated poor OS and recurrence-free survival (RFS) in patients with hepatocellular carcinoma, based on a meta-analysis of 10 studies. Notably, due to the fact that CRP is not specific for patients with CC. The level of CRP, as discussed above, could also offer insight into the prognosis of various types of cancer. In this case, the high level of CRP could serve as a prognostic marker only for those patients with CC without other cancers and treatment histories.</p>
<p>The present meta-analysis does have some limitations. First, the included studies were all of a retrospective nature, which may have introduced selection bias. Second, the cut-off values of CRP levels ranged from 2.6&#x2013;10, but we were unable to determine an optimal cut-off value for determining the prognosis of CC based on CRP levels. Third, our sample size was relatively small, as many of the included studies had relatively small sample sizes. Therefore, larger prospective studies are required to verify our findings.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusions</title>
<p>In conclusion, the results of the present meta-analysis indicated that increased CRP levels are markedly associated with poor OS and PFS in patients with CC. Therefore, serum CRP level could be an inexpensive and independent factor for determining the prognoses of patients with CC in a clinical setting.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>SY conceived and designed the study, interpreted the data, and drafted the manuscript. ZZ and LS designed and revised the manuscript. SY and ZZ selected the articles. ZZ and LS retrieved the data. All authors contributed to the article and approved the submitted version.</p>
</sec>
</body>
<back>
<sec id="s8" sec-type="funding-information">
<title>Funding</title>
<p>This study was supported by Huzhou Science and Technology Plan Project (Grant No. 2022GY45).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We would like to thank Editage (<ext-link ext-link-type="uri" xlink:href="http://www.editage.com">www.editage.com</ext-link>) for English language editing.</p>
</ack>
<sec id="s9" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr">
<p>CRP, C-reactive protein; CC, cervical cancer; HR, hazard ratio; CI, confidence interval; OS, overall survival; PFS, progression-free survival; HPV, human papillomavirus; FIGO, International Federation of Gynecology and Obstetrics; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses; NOS, Newcastle-Ottawa Scale; REM, random-effects model; FEM, fixed-effects model.</p>
</fn>
</fn-group>
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