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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Med.</journal-id>
<journal-title>Frontiers in Medicine</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Med.</abbrev-journal-title>
<issn pub-type="epub">2296-858X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fmed.2021.743406</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Medicine</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Genetic Mutations in <italic>TNFSF11</italic> Were Associated With the Chronicity of Hepatitis C Among Chinese Han Population</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Huang</surname> <given-names>Peng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1069067/overview"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Hou</surname> <given-names>Yu-Qing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn002"><sup>&#x02020;</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Wu</surname> <given-names>Jing-Jing</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Yi-Di</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Ye</surname> <given-names>Xiang-Yu</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Zang</surname> <given-names>Feng</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Yu</surname> <given-names>Rong-Bin</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/1109803/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name><surname>Yang</surname> <given-names>Sheng</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x0002A;</sup></xref>
<uri xlink:href="http://loop.frontiersin.org/people/895157/overview"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Environmental Health, Yangzhou Center for Disease Control and Prevention</institution>, <addr-line>Yangzhou</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Infection Management Office of the First Affiliated Hospital of Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Department of Biostatistics, School of Public Health, Nanjing Medical University</institution>, <addr-line>Nanjing</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Jian Wu, Zhejiang University, China</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Hamid Asadzadeh, Shahid Beheshti University of Medical Sciences, Iran; Hai-Feng Pan, Anhui Medical University, China; Cheng Guo, Tongji Hospital Affiliated to Tongji University, China</p></fn>
<corresp id="c001">&#x0002A;Correspondence: Sheng Yang <email>yangsheng&#x00040;njmu.edu.cn</email></corresp>
<fn fn-type="other" id="fn001"><p>This article was submitted to Infectious Diseases - Surveillance, Prevention and Treatment, a section of the journal Frontiers in Medicine</p></fn>
<fn fn-type="equal" id="fn002"><p>&#x02020;These authors have contributed equally to this work</p></fn></author-notes>
<pub-date pub-type="epub">
<day>01</day>
<month>10</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>8</volume>
<elocation-id>743406</elocation-id>
<history>
<date date-type="received">
<day>18</day>
<month>07</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>07</day>
<month>09</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2021 Huang, Hou, Wu, Wang, Ye, Zang, Yu and Yang.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Huang, Hou, Wu, Wang, Ye, Zang, Yu and Yang</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><p><bold>Background:</bold> Recently, several studies have reported that the host immune response can be related to the RANKL/RANK/OPG signaling pathway. However, the associations of <italic>TNFSF11, TNFRSF11A</italic>, and <italic>TNFRSF11B</italic> gene polymorphisms in the RANKL/RANK/OPG pathway with hepatitis C virus (HCV) infection outcomes remain unclear.</p>
<p><bold>Methods:</bold> In this case-control study, 768 persistent HCV infection and 503 spontaneous HCV clearance cases, and 1,259 control subjects were included. The Taman-MGB probe method was utilized to detect <italic>TNFSF11</italic> rs9525641, <italic>TNFRSF11A</italic> rs8686340, and <italic>TNFRSF11B</italic> rs2073618 genotypes. The distribution of three single nucleotide polymorphisms (SNPs) genotypes was analyzed using stata14.0.</p>
<p><bold>Results:</bold> SNPs rs9525641, rs8086340, and rs2073618 genotype frequencies followed the Hardy-Weinberg natural population equilibrium (<italic>p</italic> = 0.637, 0.250, and 0.113, respectively). Also, rs9525641 was significantly associated with HCV chronicity risk in recessive (OR = 1.203, 95% CI: 1.018&#x02013;1.420, <italic>p</italic> = 0.030) and additive models (OR = 1.545, 95% CI: 1.150&#x02013;2.075, <italic>p</italic> = 0.004). The stratified analysis showed that rs9525641 variant genotypes were associated with HCV chronicity among people older than 50 years (OR =1.562, 95% CI: 1.079&#x02013;2.262, <italic>p</italic> = 0.018), females (OR = 1.667, 95% CI: 1.145&#x02013;2.429, <italic>p</italic> = 0.008), ALT &#x0003C;40 U/L (OR = 1.532, 95% CI: 1.074&#x02013;2.286, <italic>p</italic> = 0.018), and AST &#x0003C; 40 U/L (OR = 1.552, 95% CI: 1.095&#x02013;2.201, <italic>p</italic> = 0.014).</p>
<p><bold>Conclusion:</bold> <italic>TNFRSF11</italic> rs9525641 was significantly associated with HCV chronicity in the Chinese population.</p></abstract>
<kwd-group>
<kwd>gene polymorphism</kwd>
<kwd>hepatitis C virus</kwd>
<kwd>TNFSF11</kwd>
<kwd>chronicity</kwd>
<kwd>bioinformatics</kwd>
</kwd-group>
<contract-num rid="cn001">BK20171054</contract-num>
<contract-num rid="cn002">2019FA005</contract-num>
<contract-num rid="cn003">81703273</contract-num>
<contract-num rid="cn004">PAPD</contract-num>
<contract-sponsor id="cn001">Natural Science Foundation of Jiangsu Province<named-content content-type="fundref-id">10.13039/501100004608</named-content></contract-sponsor>
<contract-sponsor id="cn002">Natural Science Foundation of Yunnan Province<named-content content-type="fundref-id">10.13039/501100005273</named-content></contract-sponsor>
<contract-sponsor id="cn003">Foundation for Innovative Research Groups of the National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100012659</named-content></contract-sponsor>
<contract-sponsor id="cn004">Priority Academic Program Development of Jiangsu Higher Education Institutions<named-content content-type="fundref-id">10.13039/501100012246</named-content></contract-sponsor>
<counts>
<fig-count count="0"/>
<table-count count="3"/>
<equation-count count="0"/>
<ref-count count="31"/>
<page-count count="8"/>
<word-count count="5436"/>
</counts>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>With the introduction of all-oral direct-actingantiviral therapy, a substantial breakthrough has been made in chronic HCV infection treatment during the past decades (<xref ref-type="bibr" rid="B1">1</xref>). However, due to HCV&#x00027;s significant heterogeneity and high variability, virus reinfection following successful treatment remains an important public health problem (<xref ref-type="bibr" rid="B2">2</xref>). The HCV pathogenesis and progression are complex and interact with its biological characteristics, environmental behavior factors, host immunity, and genetic background.</p>
<p>The activation of NF-&#x003BA;B and NF-&#x003BA;B-dependent inflammatory pathways are important to chronic HCV infection and its related cirrhosis and HCC. The NF-&#x003BA;B ligand (RANKL) receptor activator, a 316-amino acid transmembrane protein, is highly expressed in different immune cells including T or dendritic cells. RANKL can be induced by inflammatory factors such as interleukin 1, tumor necrosis factor &#x003B1;, and transforming growth factor &#x003B2; (<xref ref-type="bibr" rid="B3">3</xref>). Besides, RANK and osteoprotegerin (OPG) are RANKL receptors, and the RANKL/RANK/OPG pathway is important for cellular immune responses such as cell death and proliferation, inflammation, and immunity (<xref ref-type="bibr" rid="B4">4</xref>). The communication pathways mediated by TNFSF/TNFRSF are essential for numerous developmental, homeostatic, and stimulus-responsive processes. Both innate and adaptive immune cells are controlled by TNFSF/TNFRSF members in a manner that is crucial for the coordination of various mechanisms driving either co-stimulation or co-inhibition of the immune response (<xref ref-type="bibr" rid="B5">5</xref>). Different cellular immune responses can be triggered by genetic differences between different individuals. During the past decades, many studies have identified that immune cytokines SNPs were significantly associated with HCV spontaneous clearance and virological response, except for viral and environmental factors (<xref ref-type="bibr" rid="B6">6</xref>&#x02013;<xref ref-type="bibr" rid="B9">9</xref>). Studies have shown that multiple TNFSF and TNFRSF gene SNPs are related to autoimmune diseases, suggesting that these SNPs play an important role in immunity. rs8086340 and rs2073618 are closely related to the occurrence of rheumatoid arthritis (<xref ref-type="bibr" rid="B10">10</xref>), and rs9525641 may affect the susceptibility and severity of AS disease (<xref ref-type="bibr" rid="B11">11</xref>). However, no research has addressed <italic>TNFSF</italic> and <italic>TNFRSF</italic> genetic polymorphisms&#x00027; impacts on HCV-related chronic liver diseases. Considering that China has the largest number of HCV infections (about 10 million patients), we examined the relationships between OPG-RANKL-RANL pathway genes SNPs rs9525641, rs2073618, and rs8686340, and HCV infection outcomes in a high-risk Chinese population.</p>
</sec>
<sec sec-type="methods" id="s2">
<title>Methods</title>
<sec>
<title>Study Participants</title>
<p>This study included three HCV infection high-risk groups. In the present study, 2,800 subjects were recruited from 2008 to 2016, including 722 hemodialysis patients from 9 hemodialysis centers in southern China, 459 drug users from a Nanjing compulsory detoxification center, and 1,619 paid blood donors from 6 Zhenjiang villages. All research objects voluntarily signed the informed consent. The exclusion criteria were: (1) patients under 18 years and over 80 years; (2) patients with interferon treatment history; (3) patients co-infected with HBV and HIV; (4) patients who suffer from autoimmune diseases or malignant tumors; (5) patients with other liver diseases. All patients were diagnosed with patient&#x00027;s clinical symptoms and biochemical examination indicators by experienced doctors, strictly following international standards. Patients were grouped according to their HCV antibodies and viral load. The participants were categorized into three groups: (1) A, uninfected control (anti-HCV and HCV RNA negative); (2) B, spontaneous HCV clearance (anti-HCV positive and HCV RNA negative); (3) C, persistent HCV infection cases (anti-HCV and HCV RNA positive).</p>
<p>This study was conducted strictly under the &#x0201C;Declaration of Helsinki&#x0201D; and was approved by the Ethics Committee of Nanjing Medical University (2017445).</p>
<p>We interviewed each participant with trained personnel and collected demographic data and environmental exposure history information through a structured survey. All participants were informed and agreed to participate in this study before recruitment.</p>
</sec>
<sec>
<title>Data and Blood Sample Collection</title>
<p>After the interview, we collected venous blood samples (&#x0007E;10 mL) from each participant, separated the plasma and white blood cells, and stored them at &#x02212;80&#x000B0;C until assays. The detection of subjects&#x00027; anti-HCV antibodies, HCV RNA, and HCV genotypes was performed with Jurong City People&#x00027;s Hospital and Yixing City People&#x00027;s Hospital, using third-generation enzyme-linked immunosorbent assay (ELISA) (Architect Anti-HCV assay, Abbott Laboratories, Abbott Park, IL, USA), Trizol LS reagent (Takara Biotech, Tokyo, Japan) and the murex HCV serotype ELISA kit (Abbott, Wiesbaden, Germany), respectively. The HCV RNA load detection limit was 1 &#x000D7; 10<sup>3</sup> IU/ml and all serological tests were performed with the same analytical systems.</p>
</sec>
<sec>
<title>SNP Selection and Genotyping</title>
<p>Candidate <italic>TNFSF11</italic>/<italic>TNFRSF11A</italic>/<italic>TNFRSF11B</italic> gene Tag SNPs were selected by searching the 1,000 Genomes Project (<ext-link ext-link-type="uri" xlink:href="http://www.1000genomes.org/">http://www.1000genomes.org/</ext-link>) or the HapMap (<ext-link ext-link-type="uri" xlink:href="http://www.hapmap.org/">http://www.hapmap.org/</ext-link>) databases. The selected SNPs were filtered with the following criteria: (1) minor allele frequency &#x0003E;5% in Han Chinese population, acquired from the Haploview software (version 4.2; Broad Institute, Cambridge, MA, USA); (2) Hardy-Weinberg equilibrium test <italic>p</italic> &#x02265; 0.05; (3) reported SNPs from previous studies associated with immune-related disorders; (4) combined bioinformatics data from Regulome DB (<ext-link ext-link-type="uri" xlink:href="http://regulome.stanford.edu/">http://regulome.stanford.edu/</ext-link>). Finally, three SNP candidates (rs9525641, rs8086340, and rs2073618) were chosen for genotyping. Primers and probes are presented in <xref ref-type="supplementary-material" rid="SM7">Supplementary Table 1</xref>.</p>
<p>Genomic DNA was isolated from subjects&#x00027; peripheral blood leukocytes using protease K digestion, phenol-chloroform extraction, and ethanol precipitation. SNPs genotyping was performed with a Taman allelic discrimination assay on the LightCycler&#x000AE; 480 IIReal-Time PCR System (Roche, Switzerland). All genotyping was performed without knowing the subjects&#x00027; case or control status. Each SNP accordance rate was 100% for the repeated experiments of 10% random samples. Additionally, the genotyping success rates for these polymorphisms were above 95%. All tests were carried out following the manufacturer&#x00027;s instructions and were performed with the same analytical systems.</p>
</sec>
<sec>
<title><italic>In silico</italic> Analysis</title>
<p>The function of SNPs was predicted using the Regulome DB online database, HaploReg database (<ext-link ext-link-type="uri" xlink:href="https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php">https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php</ext-link>) and Vienna RNA Web Servers (<ext-link ext-link-type="uri" xlink:href="http://rna.tbi.univie.ac.at/cgi-bin/RNAWeb/Suite/RNAfold.cgi">http://rna.tbi.univie.ac.at/cgi-bin/RNAWeb/Suite/RNAfold.cgi</ext-link>). The Regulome DB online database annotated SNPs with known and predicted regulatory elements in <italic>Homo sapiens</italic> genome intergenic regions was used to obtain SNPs&#x00027; Regulome DB scores. Different Regulome DB score represents different combinations of the above and detailed information on all Regulome DB scores (<xref ref-type="supplementary-material" rid="SM8">Supplementary Table 2</xref>). The HaploReg database can be used for exploring chromatin states, conservation, and regulatory motif alterations within a set of genetically linked variants. Moreover, RNA secondary structures were predicted using the Vienna RNA Web Servers based on its latest package (Version 2.3.1).</p>
</sec>
<sec>
<title>Statistic Analysis</title>
<p>The demographic and clinical data distribution among the three groups was compared using the &#x003C7;<sup>2</sup> test. HWE was assessed among control subjects by the goodness-of-fit &#x003C7;<sup>2</sup> test. Logistic regression, with age, gender, and high-risk population adjustments, was used to analyze the relationship between the three SNPs and HCV infection outcome according to four genetic models, showing odds ratio and 95% confidence intervals. Hierarchical analysis was used to control confounding factors&#x00027; effects on the results and Q tests were used to determine heterogeneity between subgroups. Statistical analyzes were performed using stata14.0, and a two-sided <italic>p</italic> &#x0003C; <italic>0.05</italic> was considered statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec>
<title>Demographic and Clinical Characteristics of Participants</title>
<p>According to the HCV antibody and RNA, subjects were divided into three groups. The demographic and clinical characteristics distribution among the HCV-uninfected control, the spontaneous HCV clearance, and the persistent infection groups are presented in <xref ref-type="table" rid="T1">Table 1</xref>. No significant differences in age or gender distribution among groups were detected (<italic>p</italic> = 0.185 and 0.095, respectively). On the other hand, alanine aminotransferase (ALT), aspartate aminotransferase (AST), infection routes, and HCV genotype differed (<italic>p</italic> &#x0003C; 0.001).</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p>Demographic and clinical characteristics among HCV control, spontaneous clearance, and persistent infection groups.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Variables</bold></th>
<th valign="top" align="center"><bold>Group A (%)</bold></th>
<th valign="top" align="center"><bold>Group B (%)</bold></th>
<th valign="top" align="center"><bold>Group C (%)</bold></th>
<th valign="top" align="center"><bold><italic>P</italic></bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 1,529</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 503</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 768</bold></th>
<th/>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left">Age (years)</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.185</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;50</td>
<td valign="top" align="center">561 (36.69)</td>
<td valign="top" align="center">193 (38.37)</td>
<td valign="top" align="center">312 (40.63)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;50</td>
<td valign="top" align="center">968 (63.31)</td>
<td valign="top" align="center">310 (61.63)</td>
<td valign="top" align="center">456 (59.38)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">Gender</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.095</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Male</td>
<td valign="top" align="center">615 (40.22)</td>
<td valign="top" align="center">194 (38.57)</td>
<td valign="top" align="center">273 (35.55)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Female</td>
<td valign="top" align="center">914 (59.78)</td>
<td valign="top" align="center">309 (61.43)</td>
<td valign="top" align="center">495 (64.45)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">ALT (U/L)</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;40</td>
<td valign="top" align="center">1,437 (94.91)</td>
<td valign="top" align="center">393 (78.29)</td>
<td valign="top" align="center">443 (57.83)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;40</td>
<td valign="top" align="center">77 (5.09)</td>
<td valign="top" align="center">109 (21.71)</td>
<td valign="top" align="center">323 (42.17)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="5">AST (U/L)</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;40</td>
<td valign="top" align="center">1,438 (95.11)</td>
<td valign="top" align="center">401 (81.17)</td>
<td valign="top" align="center">454 (59.97)</td>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;40</td>
<td valign="top" align="center">74 (4.89)</td>
<td valign="top" align="center">93 (18.83)</td>
<td valign="top" align="center">303 (40.03)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">High-risk population</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;HD</td>
<td valign="top" align="center">555 (36.30)</td>
<td valign="top" align="center">91 (18.09)</td>
<td valign="top" align="center">76 (9.90)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;IVDU</td>
<td valign="top" align="center">181 (11.84)</td>
<td valign="top" align="center">138 (27.44)</td>
<td valign="top" align="center">140 (18.23)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;PBD</td>
<td valign="top" align="center">793 (51.86)</td>
<td valign="top" align="center">274 (54.47)</td>
<td valign="top" align="center">552 (71.88)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">HCV genotype</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">&#x0003C;0.001</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;1b</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">42 (26.25)</td>
<td valign="top" align="center">223 (46.07)</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Non-1b</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">118 (73.75)</td>
<td valign="top" align="center">261 (53.93)</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>Group A: controls; Group B: spontaneous clearance subjects; Group C: persistent infection patients.</italic></p> 
<p><italic>Non-1b means viral strains other than 1b, including genotype 1a, 2, and 3 (either solely or mixed infection).</italic></p>
<p><italic>HCV, hepatitis C virus; SD, standard deviation; ALT, alanine transaminase; AST, aspartate transaminase; HD, hemodialysis patients; IVDU, Intravenous drug user; PBD, paid blood donors</italic>.</p>
</table-wrap-foot>
</table-wrap>
<p>SNPs rs9525641, rs8086340, and rs2073618 genotype frequencies in the HCV-uninfected control followed the Hardy-Weinberg equilibrium (<italic>p</italic> = 0.637, 0.250, and 0.113, respectively). This indicated that the control was a representative group.</p>
</sec>
<sec>
<title>Associations Between SNP Candidates and HCV Infection Outcomes</title>
<p>The genotype distribution rs9525641, rs8086340, and rs2073618 among groups are shown in <xref ref-type="table" rid="T2">Table 2</xref>. To analyze the association between these SNPs and HCV infection susceptibility, we first combined patients from groups B and C in an HCV-infected group and compared it with the control (A). However, no significant association was observed in the logistic regression analyses between these three SNPs and HCV infection susceptibility (<italic>p</italic> &#x0003E; 0.05). To determine the association between these SNPs and HCV chronicity, the B group was selected as a control and compared to C. The regression analysis of a co-dominant model&#x02014;corrected for age, gender, and high-risk population&#x02014;showed that patients carrying the rs9525641-C gene were significantly associated with HCV chronic diseases (adjusted OR = 1.518, 95% CI: 1.079&#x02013;2.136, <italic>p</italic> = 0.017), as for a recessive (adjusted OR = 1.203, 95% CI: 1.018&#x02013;1.420, <italic>p</italic> = 0.030), and additive models (adjusted OR = 1.545, 95% CI: 1.150&#x02013;2.075, <italic>p</italic> = 0.004). However, no correlation was observed between the two other genotypes and HCV infection chronicity (<italic>p</italic> &#x0003E; 0.05).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p>Genotypes distributions of three SNPs among persistent infection, spontaneous clearance and control group.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>SNPs (genotype)</bold></th>
<th valign="top" align="center"><bold>Group A <italic>n</italic> (%)</bold></th>
<th valign="top" align="center"><bold>Group B <italic>n</italic> (%)</bold></th>
<th valign="top" align="center"><bold>Group C <italic>n</italic> (%)</bold></th>
<th valign="top" align="center"><bold>OR(95%CI)<sup><bold>a</bold></sup></bold></th>
<th valign="top" align="center"><bold><italic>P</italic><sup><bold>a</bold></sup></bold></th>
<th valign="top" align="center"><bold>OR (95%CI)<sup><bold>b</bold></sup></bold></th>
<th valign="top" align="center"><bold><italic>P</italic><sup><bold>b</bold></sup></bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 1,529</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 503</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> &#x0003D; 768</bold></th>
<th/>
<th/>
<th/>
<th/>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left"><italic>rs9525641</italic></td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.037</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">TT</td>
<td valign="top" align="center">406 (26.71)</td>
<td valign="top" align="center">144 (29.63)</td>
<td valign="top" align="center">209 (27.68)</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">TC</td>
<td valign="top" align="center">768 (50.53)</td>
<td valign="top" align="center">260 (53.50)</td>
<td valign="top" align="center">365 (48.34)</td>
<td valign="top" align="center">0.946 (0.788&#x02013;1.137)</td>
<td valign="top" align="center">0.556</td>
<td valign="top" align="center">0.973 (0.743&#x02013;1.274)</td>
<td valign="top" align="center">0.842</td>
</tr>
<tr>
<td valign="top" align="left">CC</td>
<td valign="top" align="center">346 (22.76)</td>
<td valign="top" align="center">82 (16.87)</td>
<td valign="top" align="center">181 (23.97)</td>
<td valign="top" align="center">0.892 (0.715&#x02013;1.114)</td>
<td valign="top" align="center">0.313</td>
<td valign="top" align="center"><bold>1.518 (1.079</bold>&#x02013;<bold>2.136)</bold></td>
<td valign="top" align="center"><bold>0.017</bold></td>
</tr>
<tr>
<td valign="top" align="left">Dominant model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.930 (0.782&#x02013;1.105)</td>
<td valign="top" align="center">0.409</td>
<td valign="top" align="center">1.104 (0.855&#x02013;1.425)</td>
<td valign="top" align="center">0.448</td>
</tr>
<tr>
<td valign="top" align="left">Recessive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.925 (0.767&#x02013;1.115)</td>
<td valign="top" align="center">0.412</td>
<td valign="top" align="center"><bold>1.545 (1.150</bold>&#x02013;<bold>2.075)</bold></td>
<td valign="top" align="center"><bold>0.004</bold></td>
</tr>
<tr>
<td valign="top" align="left">Additive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.945 (0.846&#x02013;1.055)</td>
<td valign="top" align="center">0.313</td>
<td valign="top" align="center"><bold>1.203 (1.018</bold>&#x02013;<bold>1.420)</bold></td>
<td valign="top" align="center"><bold>0.030</bold></td>
</tr>
<tr>
<td valign="top" align="left"><italic>rs8086340</italic></td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.267</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">GG</td>
<td valign="top" align="center">621 (41.40)</td>
<td valign="top" align="center">212 (43.71)</td>
<td valign="top" align="center">332 (43.92)</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">GC</td>
<td valign="top" align="center">704(46.93)</td>
<td valign="top" align="center">209 (43.09)</td>
<td valign="top" align="center">322 (42.59)</td>
<td valign="top" align="center">0.878 (0.743&#x02013;1.036)</td>
<td valign="top" align="center">0.124</td>
<td valign="top" align="center">0.987 (0.769&#x02013;1.265)</td>
<td valign="top" align="center">0.916</td>
</tr>
<tr>
<td valign="top" align="left">CC</td>
<td valign="top" align="center">175 (11.67)</td>
<td valign="top" align="center">64 (13.20)</td>
<td valign="top" align="center">102 (13.20)</td>
<td valign="top" align="center">1.140 (0.887&#x02013;1.465)</td>
<td valign="top" align="center">0.306</td>
<td valign="top" align="center">1.024 (0.713&#x02013;1.472)</td>
<td valign="top" align="center">0.896</td>
</tr>
<tr>
<td valign="top" align="left">Dominant model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.929 (0.794&#x02013;1.086)</td>
<td valign="top" align="center">0.355</td>
<td valign="top" align="center">0.995 (0.788&#x02013;1.257)</td>
<td valign="top" align="center">0.970</td>
</tr>
<tr>
<td valign="top" align="left">Recessive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.219 (0.963&#x02013;1.543)</td>
<td valign="top" align="center">0.100</td>
<td valign="top" align="center">1.031 (0.733&#x02013;1.450)</td>
<td valign="top" align="center">0.860</td>
</tr>
<tr>
<td valign="top" align="left">Additive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.007 (0.898&#x02013;1.130)</td>
<td valign="top" align="center">0.902</td>
<td valign="top" align="center">1.005 (0.850&#x02013;1.188)</td>
<td valign="top" align="center">0.952</td>
</tr>
<tr>
<td valign="top" align="left"><italic>rs2073618</italic></td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.149</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td valign="top" align="left">GG</td>
<td valign="top" align="center">854 (56.33)</td>
<td valign="top" align="center">266 (53.41)</td>
<td valign="top" align="center">409 (53.46)</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
<td valign="top" align="center">1.00</td>
<td valign="top" align="center">&#x02013;</td>
</tr>
<tr>
<td valign="top" align="left">GC</td>
<td valign="top" align="center">552 (36.41)</td>
<td valign="top" align="center">207 (41.57)</td>
<td valign="top" align="center">305 (39.87)</td>
<td valign="top" align="center">1.131 (0.962&#x02013;1.330)</td>
<td valign="top" align="center">0.137</td>
<td valign="top" align="center">0.933 (0.735&#x02013;1.185)</td>
<td valign="top" align="center">0.572</td>
</tr>
<tr>
<td valign="top" align="left">CC</td>
<td valign="top" align="center">110 (110)</td>
<td valign="top" align="center">25 (5.02)</td>
<td valign="top" align="center">51 (6.67)</td>
<td valign="top" align="center">0.841 (0.613&#x02013;1.155)</td>
<td valign="top" align="center">0.286</td>
<td valign="top" align="center">1.318 (0.791&#x02013;2.197)</td>
<td valign="top" align="center">0.289</td>
</tr>
<tr>
<td valign="top" align="left">Dominant model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.082 (0.927&#x02013;1.264)</td>
<td valign="top" align="center">0.316</td>
<td valign="top" align="center">0.975 (0.774&#x02013;1.227)</td>
<td valign="top" align="center">0.827</td>
</tr>
<tr>
<td valign="top" align="left">Recessive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.799 (0.586&#x02013;1.090)</td>
<td valign="top" align="center">0.157</td>
<td valign="top" align="center">1.358 (0.823&#x02013;2.239)</td>
<td valign="top" align="center">0.230</td>
</tr>
<tr>
<td valign="top" align="left">Additive model</td>
<td/>
<td/>
<td/>
<td valign="top" align="center">1.015 (0.896&#x02013;1.149)</td>
<td valign="top" align="center">0.817</td>
<td valign="top" align="center">1.029 (0.851&#x02013;1.243)</td>
<td valign="top" align="center">0.770</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>CI, confidence interval; HCV, hepatitis C virus; OR, odds ratio; SNP, single nucleotide polymorphism.</italic></p> 
<p><italic>Group A: controls; Group B: spontaneous clearance subjects; Group C: persistent infection patients. Group (B&#x0002B;C): Infected individuals.</italic></p>
<p><italic><sup>a</sup>The P-value, OR and 95% CIs of Group (B &#x0002B; C) vs. Group A were calculated on the basis of the logistic regression model, adjusted by gender, age, and high-risk population.</italic></p>
<p><italic><sup>b</sup>The P-value, OR and 95% CIs of Group C vs. Group B were calculated on the basis of the logistic regression model, adjusted by gender, age, high-risk population.</italic></p>
<p><italic>Bold type indicates statistically significant results</italic>.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Stratified Analysis</title>
<p>To control age, gender, high-risk population, and HCV genotypes bias in each population, we performed a stratified analysis to explore the association between the rs9525641 genotype and HCV chronicity using a recessive model (<xref ref-type="table" rid="T3">Table 3</xref>). Results showed that the rs9525641 variant genotypes were significantly associated with an increased chronic HCV infection risk among people &#x02265;50 years (adjusted OR = 1.562, 95% CI: 1.079&#x02013;2.262, <italic>p</italic> = 0.018), females (adjusted OR = 1.667, 95% CI: 1.145&#x02013;2.429, <italic>p</italic> = 0.008), ALT &#x0003C;40 U/L (adjusted OR = 1.532, 95% CI: 1.074&#x02013;2.286, <italic>p</italic> = 0.018), and AST &#x0003C;40 U/L (adjusted OR = 1.552, 95% CI: 1.095&#x02013;2.201, <italic>p</italic> = 0.014). Additionally, considering data heterogeneity in different subgroups, we performed a heterogeneity test. Results showed no significant heterogeneity between groups (<italic>p</italic> &#x0003E; 0.05).</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p>Stratified analysis the association of rs9525641 with HCV chronicity.</p></caption>
<table frame="hsides" rules="groups">
<thead><tr>
<th valign="top" align="left"><bold>Subgroups</bold></th>
<th valign="top" align="center"><bold>Group A</bold></th>
<th valign="top" align="center"><bold>Group B</bold></th>
<th valign="top" align="center"><bold>Group C</bold></th>
<th valign="top" align="center"><bold>OR (95%CI)<sup><bold>a</bold></sup></bold></th>
<th valign="top" align="center"><bold><italic>P</italic><sup><bold>a</bold></sup></bold></th>
<th valign="top" align="center"><bold><italic>P<sup><bold><italic>b</italic></bold></sup></italic></bold></th>
</tr>
<tr>
<th/>
<th valign="top" align="center"><bold><italic>n</italic> (CC/CA/AA)</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> (CC/CA/AA)</bold></th>
<th valign="top" align="center"><bold><italic>n</italic> (CC/CA/AA)</bold></th>
<th/>
<th/>
<th/>
</tr>
</thead>
<tbody>
<tr>
<td valign="top" align="left" colspan="7">Age</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;50</td>
<td valign="top" align="center">145/284/126</td>
<td valign="top" align="center">51/100/28</td>
<td valign="top" align="center">80/155/65</td>
<td valign="top" align="center">1.482(0.900&#x02013;2.442)</td>
<td valign="top" align="center">0.122</td>
<td valign="top" align="center">0.872</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;50</td>
<td valign="top" align="center">261/484/220</td>
<td valign="top" align="center">93/160/54</td>
<td valign="top" align="center">129/210/116</td>
<td valign="top" align="center"><bold>1.562 (1.079</bold>&#x02013;<bold>2.262)</bold></td>
<td valign="top" align="center"><bold>0.018</bold></td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="7">Gender</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Male</td>
<td valign="top" align="center">179/300/133</td>
<td valign="top" align="center">58/92/33</td>
<td valign="top" align="center">81/126/64</td>
<td valign="top" align="center">1.330 (0.823&#x02013;2.150)</td>
<td valign="top" align="center">0.245</td>
<td valign="top" align="center">0.474</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Female</td>
<td valign="top" align="center">277/468/213</td>
<td valign="top" align="center">86/168/49</td>
<td valign="top" align="center">128/239/117</td>
<td valign="top" align="center"><bold>1.667 (1.145</bold>&#x02013;<bold>2.429)</bold></td>
<td valign="top" align="center"><bold>0.008</bold></td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="7">ALT (U/L)</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;40</td>
<td valign="top" align="center">383/722/326</td>
<td valign="top" align="center">115/203/63</td>
<td valign="top" align="center">124/211/100</td>
<td valign="top" align="center"><bold>1.532 (1.074</bold>&#x02013;<bold>2.186)</bold></td>
<td valign="top" align="center"><bold>0.018</bold></td>
<td valign="top" align="center">0.884</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;40</td>
<td valign="top" align="center">21/37/16</td>
<td valign="top" align="center">29/56/19</td>
<td valign="top" align="center">85/152/81</td>
<td valign="top" align="center">1.455 (0.826&#x02013;2.562)</td>
<td valign="top" align="center">0.176</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="7">AST (U/L)</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0; &#x0003C;40</td>
<td valign="top" align="center">380/729/321</td>
<td valign="top" align="center">112/213/65</td>
<td valign="top" align="center">130/210/106</td>
<td valign="top" align="center"><bold>1.552 (1.095</bold>&#x02013;<bold>2.201)</bold></td>
<td valign="top" align="center"><bold>0.014</bold></td>
<td valign="top" align="center">0.736</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;&#x02265;40</td>
<td valign="top" align="center">22/30/21</td>
<td valign="top" align="center">30/43/17</td>
<td valign="top" align="center">79/147/73</td>
<td valign="top" align="center">1.373 (0.751&#x02013;2.510)</td>
<td valign="top" align="center">0.303</td>
<td/>
</tr>
<tr>
<td valign="top" align="left" colspan="7">High-risk population</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;HD</td>
<td valign="top" align="center">135/286/131</td>
<td valign="top" align="center">26/44/17</td>
<td valign="top" align="center">21/37/17</td>
<td valign="top" align="center">1.337 (0.610&#x02013;2.928)</td>
<td valign="top" align="center">0.468</td>
<td valign="top" align="center">0.614</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;IVDU</td>
<td valign="top" align="center">46/93/38</td>
<td valign="top" align="center">37/74/15</td>
<td valign="top" align="center">41/67/32</td>
<td valign="top" align="center">2.263 (1.152&#x02013;4.445)</td>
<td valign="top" align="center">0.018</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;PBD</td>
<td valign="top" align="center">225/389/177</td>
<td valign="top" align="center">81/142/50</td>
<td valign="top" align="center">147/261/132</td>
<td valign="top" align="center">1.419 (0.983&#x02013;2.047)</td>
<td valign="top" align="center">0.062</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">HCV genotypes</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td valign="top" align="center">0.708</td>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;1b</td>
<td valign="top" align="center">&#x02014;</td>
<td valign="top" align="center">7/24/9</td>
<td valign="top" align="center">72/99/51</td>
<td valign="top" align="center">0.996 (0.347&#x02013;2.858)</td>
<td valign="top" align="center">0.994</td>
<td/>
</tr>
<tr>
<td valign="top" align="left">&#x000A0;Non-1b</td>
<td valign="top" align="center">&#x02014;</td>
<td valign="top" align="center">38/54/19</td>
<td valign="top" align="center">57/136/58</td>
<td valign="top" align="center">1.302 (0.643&#x02013;2.637)</td>
<td valign="top" align="center">0.464</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><italic>CI, confidence interval; HCV, hepatitis C virus; OR, odds ratio; HD, hemodialysis patients; IVDU, Intravenous drug user; PBD, paid blood donors; Non-1b, viral strains other than 1b, including genotype 1a, 2, and 3 (either solely or mixed infection).</italic></p> 
<p><italic>Group A: controls; Group B: spontaneous clearance subjects; Group C: persistent infection patients. Group (B&#x0002B;C): Infected individuals.</italic></p>
<p><italic><sup>a</sup>The P-value, OR and 95% CIs of Group C vs. Group B were calculated on the basis of the logistic regression model, adjusted by gender, age, high-risk population.</italic></p>
<p><italic><sup>b</sup>P-value for the heterogeneity test.</italic></p>
<p><italic>Bold type indicates statistically significant results</italic>.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec>
<title>Bioinformatics Analysis</title>
<p>The rs9525641 genotype had a Regulome DB score of 5, suggesting its potential functions such as transcription factor binding or DNase peak. Based on the HaploReg database, rs9525641 overlaps promoter histone marks, enhancer histone marks, and DNase and FXR motifs. Furthermore, 22 SNPs were in linkage disequilibrium with rs9525641 in the Asian population (<italic>r</italic><sup>2</sup> &#x0003E; 0.8) (available at HaploReg database). Results of the 22 SNPs are presented in <xref ref-type="supplementary-material" rid="SM9">Supplementary Table 3</xref>. The in?uence of those SNPs on the RANKL mRNA secondary structure was predicted using the RNAfold Web Server. Six SNPs presented local structure changes (rs17458177, rs1325799, rs17536328, rs7984870, rs9533155, and rs3742257) (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figures 1</xref>&#x02013;<xref ref-type="supplementary-material" rid="SM6">6</xref>). Moreover, rs17458177-C and -T alleles showed a difference in the lowest free energy (&#x02212;18.40 vs. &#x02212;18.90 kcal/mol), suggesting that mutations might affect RANKL transcription. Specific information for the other SNPs can be found in the <xref ref-type="supplementary-material" rid="SM1">Supplementary Figures 1</xref>&#x02013;<xref ref-type="supplementary-material" rid="SM6">6</xref>.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>The RANKL message is detected in the peripheral lymph nodes and bone marrow, thymus, spleen, Peyer&#x00027;s patches, brain, heart, skin, skeletal muscle, kidney, liver, lung, and mammary tissues (<xref ref-type="bibr" rid="B12">12</xref>). The RANKL/RANK system has been shown to play a critical role in the immune system, including lymph-node development, lymphocyte differentiation, dendritic cell survival, and T-cell activation and tolerance induction (<xref ref-type="bibr" rid="B13">13</xref>). RANKL can regulate lymph-node organogenesis, T- and B-lymphocyte development, and osteoclast differentiation. Some studies also indicated that RANKL regulates the thymus microenvironment by autoimmune regulators expression activation (<xref ref-type="bibr" rid="B14">14</xref>). Additionally, at the molecular level, RANK interacts with RANKL to activate the transcription factor NF-&#x003BA;B along with TNF receptor-related factor family signaling molecules (<xref ref-type="bibr" rid="B15">15</xref>). Considering that the NF-&#x003BA;B function is related to RANKL/RANK, and NF-&#x003BA;B has been linked to chronic hepatitis C (<xref ref-type="bibr" rid="B16">16</xref>&#x02013;<xref ref-type="bibr" rid="B18">18</xref>), we hyphothesized that the RANKL/RANK pathway polymorphisms would affect the HCV infection outcome.</p>
<p>RANKL, RANK, and OPG are encoded by <italic>TNFSF11</italic> (gene map locus 13q14), <italic>TNFRSF11A</italic> (gene map locus 18q22.1), and <italic>TNFRSF11B</italic> (gene map locus 8q24), respectively (<xref ref-type="bibr" rid="B19">19</xref>). The <italic>TNFSF11</italic> gene structure is highly conserved among mammals, consisting of five exons that span 33.9 kb in humans (<xref ref-type="bibr" rid="B10">10</xref>). SNPs located near <italic>TNFSF11, TNFRSF11A</italic>, and <italic>TNFRSF11B</italic> have been reported to be closely associated with Paget&#x00027;s disease (<xref ref-type="bibr" rid="B20">20</xref>), osteoporotic fractures (<xref ref-type="bibr" rid="B21">21</xref>), cardiovascular diseases (<xref ref-type="bibr" rid="B22">22</xref>), ankylosing spondylitis (<xref ref-type="bibr" rid="B11">11</xref>), and breast (<xref ref-type="bibr" rid="B23">23</xref>), and esophageal cancers (<xref ref-type="bibr" rid="B24">24</xref>). In this study, we showed that subjects who carried the rs9525641-C allele were more likely to develop HCV chronicity than those with the rs9525641-T allele. Furthermore, in the stratification analyses based on age, gender, and high-risk population, we found that the rs9525641-C allele was associated with HCV chronicity among elders, females, and persons with ALT and AST &#x0003C;40 U/L. Interestingly, RANKL can cause various degenerative bone diseases, such as rheumatoid arthritis and osteoporosis. These diseases are mostly female. Our research shows that RANKL is related to the chronicity of HCV in women. This suggests to some extent the combined effect of gender factors and the RANKL system in disease progression, which is worthy of further discussion. However, the heterogeneity test showed no significant heterogeneity in any pair-wise comparison (<italic>p</italic> &#x0003E; 0.05), indicating that these variables did not materially affect the results.</p>
<p>Although rs9525641 is located in the <italic>TNFSF11</italic> intronic region, this variant might play an important role in gene transcription regulation or might be in linkage disequilibrium with other functional SNPs, such as rs17458177, rs1325799, rs17536328, rs7984870, rs9533155, and rs3742257. We also calculated the degree of linkage between the candidate SNPs, <italic>r</italic><sup>2</sup> &#x0003C; 0.1 indicates that there is no linkage disequilibrium among these three SNPs. The bioinformatics analysis indicated that these variants could regulate gene transcription, mRNA export, and protein translational efficiency. In the present study, we found two possible <italic>TNFSF11</italic> biological processes using the STRING database website prediction (<ext-link ext-link-type="uri" xlink:href="https://string-db.org/cgi/network.pl?taskId=kGEafB5GcPa7:">https://string-db.org/cgi/network.pl?taskId=kGEafB5GcPa7:</ext-link>) extracellular signal-regulated kinase 1 (ERK1) and ERK2 cascade positive regulation via <italic>TNFSF11</italic>-mediated signaling; and the tumor necrosis factor-mediated signaling pathway. It has been reported that the HCV non-enveloped particles&#x00027; intriguing cellular internalization properties can activate the ERK1/2 pathway that could be important in the HCV life cycle and infection pathogenesis (<xref ref-type="bibr" rid="B25">25</xref>). Additionally, Fletcher et al. reported that several TNF superfamily members &#x02013; including TNF-&#x003B1;, TNF-&#x003B2;, TWEAK, and LIGHT &#x02013; can promote HCV entry via NF-&#x003BA;B-mediated activation of myosin light chain kinase and tight junctions disruption (<xref ref-type="bibr" rid="B26">26</xref>). Therefore, <italic>TNFSF11</italic> may be involved in these pathways and contribute to the hepatitis C chronic process. However, these hypotheses are based on computer simulations and functional evaluations using biological assays. Thus, it should be warranted in future studies.</p>
<p>RANK is an intrinsic hematopoietic cell surface receptor that stimulates NF-&#x003BA;B receptor activation, plays a central role in T and dendritic cells, and promotes lymph node development (<xref ref-type="bibr" rid="B27">27</xref>)<sup>.</sup> Previously, several studies have found that the TNFRSF11 rs8680340 was closely related to the anti-citrullinated peptide antibody and age at natural menopause (<xref ref-type="bibr" rid="B28">28</xref>). Additionally, the <italic>TNFRSF11B</italic> rs2073618 was significantly associated with Type 2 Diabetes (<xref ref-type="bibr" rid="B29">29</xref>), rheumatoid arthritis (<xref ref-type="bibr" rid="B30">30</xref>), and volumetric bone mineral density (<xref ref-type="bibr" rid="B31">31</xref>). However, in this study, no significant association was observed between <italic>TNFRSF11A</italic> rs8086340, <italic>TNFRSF11B</italic> rs2073618, and HCV infection outcome.</p>
<p>Our study also has some limitations. First, this study was performed with the Chinese Han population, requiring further reproduction in different ethnic populations. Second, we did not collect immune markers data in the RANKL/RANK pathway and could not check the association between immune markers data and the target SNPs. Third, we did not collect complete virus subtypes and viral load data and could not check the association between virus data and the target SNPs. Hence, we should improve the collection of this part of the data in the later stagethe and possible immune mechanism requires to be further studied and verified. Furthermore, we did not explore how the functional mechanisms of these SNPs affect HCV chronicity using molecular biology approaches. Then, functional studies are required in the future to explore how these polymorphisms impact chronic HCV infection.</p>
<p>Overall, our findings suggested that the rs9525641 <italic>TNFSF11</italic> polymorphism might affect HCV chronicity in a high-risk Chinese population. Larger well-designed epidemiological studies with ethnically diverse populations and functional evaluations are warranted to confirm these findings before the effect of these variants can be fully and accurately evaluated.</p>
</sec>
<sec sec-type="data-availability" id="s5">
<title>Data Availability Statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM10">Supplementary Material</xref>, further inquiries can be directed to the corresponding author/s.</p>
</sec>
<sec id="s6">
<title>Ethics Statement</title>
<p>The studies involving human participants were reviewed and approved by the Ethics Committee of Nanjing Medical University. The patients/participants provided their written informed consent to participate in this study.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>SY and R-BY: conceptualization. Y-QH: formal analysis, investigation, and writing&#x02013;original draft. PH: funding acquisition and methodology. Y-DW and J-JW: resources. J-JW and FZ: software. SY: supervision. Y-QH and X-YY: writing&#x02013;review and editing. All authors contributed to the article and approved the submitted version.</p>
</sec>
<sec sec-type="funding-information" id="s8">
<title>Funding</title>
<p>This study was supported in part by National Natural Science Foundation of China (no. 81703273), Natural Science Foundation of Jiangsu Province (no. BK20171054), Natural Science Foundation of Yunnan Province (no. 2019FA005), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).</p>

</sec>
<sec sec-type="COI-statement" id="conf1">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#x00027;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec> </body>
<back>
<ack><p>We are grateful to all patients and workers for donating this research.</p>
</ack>
<sec sec-type="supplementary-material" id="s10">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fmed.2021.743406/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fmed.2021.743406/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Image_1.JPEG" id="SM1" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 1</label>
<caption><p>The influence of rs17458177 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 bases downstream of the mutation). The minimum free energies for the C and T allele of rs17458177 were estimated at &#x02212;18.40 and &#x02212;18.90 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Image_2.JPEG" id="SM2" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 2</label>
<caption><p>The influence of rs1325799 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the G and A allele of rs1325799 were estimated at &#x02212;11.80 and &#x02212;14.00 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Image_3.JPEG" id="SM3" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 3</label>
<caption><p>The influence of rs17536328 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the C and T allele of rs17536328 were estimated at &#x02212;17.4 and &#x02212;16.60 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Image_4.JPEG" id="SM4" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 4</label>
<caption><p>The influence of rs7984870 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the G and C allele of rs7984870 were estimated at &#x02212;14.40 and &#x02212;15.50 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Image_5.JPEG" id="SM5" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 5</label>
<caption><p>The influence of rs9533155 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the C and G allele of rs9533155 were estimated at &#x02212;14.80 and &#x02212;15.50 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Image_6.JPEG" id="SM6" mimetype="image/jpeg" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Figure 6</label>
<caption><p>The influence ofrs3742257 on the RANKL mRNA secondary structures. The arrow indicates the position of the mutation (50 bases upstream and 50 basesdownstream of the mutation). The minimum free energies for the T and C allele of rs3742257 were estimated at &#x02212;31.70 and &#x02212;27.80 kcal/mol, respectively, by RNAfold Wed Server.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Table_1.DOCX" id="SM7" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Table 1</label>
<caption><p>Probes and primers of investigated <italic>TNFSF</italic> and <italic>TNFRSF</italic> SNPs for Taman assay.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Table_1.DOCX" id="SM8" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Table 2</label>
<caption><p>The detailed information on different RegulomeDB scores.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Table_1.DOCX" id="SM9" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink">
<label>Supplementary Table 3</label>
<caption><p>Annotation of variannts with stong linkage disequilibrim with SNP rs 9525641 in HaploRegV4.1.</p></caption> </supplementary-material>
<supplementary-material xlink:href="Table_2.XLSX" id="SM10" mimetype="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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