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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.2023.1114218</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Oncology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Genetic polymorphisms in <italic>CYP4F2</italic> may be associated with lung cancer risk among females and no-smoking Chinese population</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Shi</surname>
<given-names>Hongyang</given-names>
</name>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2119061"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Yonghong</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Yu</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Fang</surname>
<given-names>Ping</given-names>
</name>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Liu</surname>
<given-names>Yun</given-names>
</name>
</contrib>
</contrib-group>
<aff id="aff1">
<institution>Department of Respiratory and Critical Care Medicine, the Second Affiliated Hospital of Xi&#x2019;an Jiaotong University</institution>, <addr-line>Xi&#x2019;an</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Dana Kristjansson, Norwegian Institute of Public Health (NIPH), Norway</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Guangzhao Qi, First Affiliated Hospital of Zhengzhou University, China; Lucia Guadalupe Taja Chayeb, National Institute of Cancerology (INCAN), Mexico</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Hongyang Shi, <email xlink:href="mailto:shihy2003@126.com">shihy2003@126.com</email>
</p>
</fn>
<fn fn-type="other" id="fn002">
<p>This article was submitted to Cancer Epidemiology and Prevention, a section of the journal Frontiers in Oncology</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>03</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>13</volume>
<elocation-id>1114218</elocation-id>
<history>
<date date-type="received">
<day>02</day>
<month>12</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>02</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Shi, Zhang, Wang, Fang and Liu</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Shi, Zhang, Wang, Fang and Liu</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>Our study aimed to explore the potential association of <italic>CYP4F2</italic> gene polymorphisms with lung cancer (LC) risk.</p>
</sec>
<sec>
<title>Methods</title>
<p>The five variants in <italic>CYP4F2</italic> were genotyped using Agena MassARRAY in 507 cases and 505 controls. Genetic models and haplotypes based on logistic regression analysis were used to evaluate the potential association between <italic>CYP4F2</italic> polymorphisms and LC susceptibility.</p>
</sec>
<sec>
<title>Results</title>
<p>This study observed that rs12459936 was linked to an increased risk of LC in no-smoking participants (allele: OR = 1.38, <italic>p</italic> = 0.035; homozygote: OR = 2.00, <italic>p</italic> = 0.035; additive: OR = 1.40, <italic>p</italic> = 0.034) and females (allele: OR = 1.64, <italic>p</italic> = 0.002; homozygote: OR = 2.57, <italic>p</italic> = 0.006; heterozygous: OR = 2.56, <italic>p</italic> = 0.001; dominant: OR = 2.56, <italic>p &lt;</italic> 0.002; additive: OR = 1.67, <italic>p</italic> = 0.002). Adversely, there was a significantly decreased LC risk for rs3093110 in no-smoking participants (heterozygous: OR = 0.56, <italic>p</italic> = 0.027; dominant: OR = 0.58, <italic>p</italic> = 0.035), rs3093193 (allele: OR = 0.66, <italic>p</italic> = 0.016; homozygote: OR = 0.33, <italic>p</italic> = 0.011; recessive: OR = 0.38, <italic>p</italic> = 0.021; additive: OR = 0.64, <italic>p</italic> = 0.014), rs3093144 (recessive: OR = 0.20, <italic>p</italic> = 0.045), and rs3093110 (allele: OR = 0.54, <italic>p</italic> = 0.010; heterozygous: OR = 0.50, <italic>p</italic> = 0.014; dominant: OR = 0.49, <italic>p</italic> = 0.010; additive: OR = 0.54, <italic>p</italic> = 0.011) in females.</p>
</sec>
<sec>
<title>Conclusions</title>
<p>The study demonstrated that <italic>CYP4F2</italic> variants were associated with LC susceptibility, with evidence suggesting that this connection may be affected by gender and smoking status.</p>
</sec>
</abstract>
<kwd-group>
<kwd>
<italic>CYP4F2</italic>
</kwd>
<kwd>lung cancer</kwd>
<kwd>single nucleotide polymorphisms</kwd>
<kwd>smoking</kwd>
<kwd>gender</kwd>
</kwd-group>
<contract-sponsor id="cn001">Natural Science Basic Research Program of Shaanxi Province<named-content content-type="fundref-id">10.13039/501100017596</named-content>
</contract-sponsor>
<counts>
<fig-count count="2"/>
<table-count count="5"/>
<equation-count count="0"/>
<ref-count count="23"/>
<page-count count="10"/>
<word-count count="4807"/>
</counts>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>Lung cancer (LC) has been regarded as one of the most common causes of cancer-related death worldwide over the past few decades, with an estimated 2.1 million new diagnoses of LC in 2018, accounting for 12% of the total increase in cancer cases (<xref ref-type="bibr" rid="B1">1</xref>). In recent years, the incidence of LC in China has been consistent with the global trend, showing a rapid increase, and LC has since become the main cause of cancer-related deaths in China (<xref ref-type="bibr" rid="B2">2</xref>). It is predicted that the mortality of LC in China is likely to increase by about 40% between 2015 and 2030 (<xref ref-type="bibr" rid="B3">3</xref>). Despite advances in early detection, the majority of LC patients are often diagnosed at a later stage, resulting in a 5-year overall survival rate of only 10% to 15%, according to statistics (<xref ref-type="bibr" rid="B4">4</xref>). The burden of LC on our society is increasing day by day and cannot be ignored. Various factors can predispose people to LC, with smoking being the most prevalent factor. In addition, other potential risk factors include gender, age, race, ethnicity, and especially single nucleotide polymorphisms (SNPs) (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>).</p>
<p>Cytochrome P450s (CYP), phase I drug metabolizing enzymes, encode 57 CYP proteins in the human genome and are responsible for the metabolism of numerous endogenous and xenobiotic compounds (<xref ref-type="bibr" rid="B7">7</xref>). The <italic>CYP4F2</italic> gene, a member of the <italic>CYP450</italic> superfamily, is an &#x3c9;-hydroxylase that catalyzes the first step of the vitamin E metabolic pathway (<xref ref-type="bibr" rid="B8">8</xref>), as well as the metabolism of arachidonic acid (AA) to generate 20-hydroxyethyl hexadecanoic acid (20-HETE) through &#x3c9;-hydroxylation (<xref ref-type="bibr" rid="B9">9</xref>). 20-HETE is known to promote tumorigenesis by increasing a variety of pro-inflammatory mediators, cytokines, and chemokines. Previous studies have demonstrated that the elevated expression of <italic>CYP4F2</italic> enzymes and 20-HETE is closely related to ovarian cancer (<xref ref-type="bibr" rid="B10">10</xref>). We hypothesized that <italic>CYP4F2</italic> might be involved in tumor genesis and development by accelerating the production of 20-HETE. Additionally, Geng et&#xa0;al. have proved that rs1558139 and rs2108622 of <italic>CYP4F2</italic> are associated with hypertension, and the association between rs1558139 and hypertension is particularly strong in men (<xref ref-type="bibr" rid="B11">11</xref>). Despite this, there is a lack of studies investigating the association between <italic>CYP4F2</italic> polymorphisms and LC risk.</p>
<p>In this case&#x2013;control study, five SNPs (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) in <italic>CYP4F2</italic> were genotyped by the Agena MassARRAY platform. The gender- and smoking-stratified analyses on the correlation between <italic>CYP4F2</italic> variants and LC risk were performed.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Study subjects</title>
<p>A total of 507 newly diagnosed LC patients (353 males and 154 females) were randomly recruited from the Second Affiliated Hospital of Xi&#x2019;an Jiaotong University in the case&#x2013;control association analysis between <italic>CYP4F2</italic> polymorphisms and the risk of LC. All patients had no history of any other cancers and had not received chemotherapy before acquiring blood samples. Further, the control group comprised 505 unrelated healthy controls (354 males and 151 females) from the physical examination center of the hospital. Information about all subjects, including age, gender, height (cm), weight (kg), smoking status, drinking status, tumor stage, and lymph node metastasis, was collected from questionnaires and clinical data. Peripheral blood samples were collected from all study subjects into vacutainer tubes containing EDTA, and genomic DNA was then isolated from the collected blood samples using the GoldMag-Mini Purification Kit (GoldMag Co. Ltd., Xi&#x2019;an, China) and stored at &#x2212;80&#xb0;C. DNA concentration and purity were determined by a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).</p>
</sec>
<sec id="s2_2">
<title>SNP selection and genotyping</title>
<p>In this study, five SNPs (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) in <italic>CYP4F2</italic> were selected according to previously published studies on the association between <italic>CYP4F2</italic> polymorphisms and disease susceptibility (<xref ref-type="bibr" rid="B12">12</xref>&#x2013;<xref ref-type="bibr" rid="B14">14</xref>). The genotype distributions of the candidate SNPs in controls met Hardy&#x2013;Weinberg equilibrium (HWE) (<italic>p &gt;</italic>0.05). All the candidate SNPs had a minor allele frequency (MAF) of &gt;5% in the Han Chinese in Beijing (CHB) population from the 1,000 Genomes Project (<ext-link ext-link-type="uri" xlink:href="http://www.internationalgenome.org/">http://www.internationalgenome.org/</ext-link>). The primers for five SNPs were designed by Agena Bioscience Assay Design Suite version 2.0 software. The polymorphisms were genotyped using the Agena MassARRAY platform (Agena Bioscience, San Diego, CA, USA) with iPLEX gold chemistry. Ultimately, Agena Bioscience TYPER version 4.0 software was used for data management and genotyping result analysis.</p>
</sec>
<sec id="s2_3">
<title>Expression analysis</title>
<p>We extracted the data for <italic>CYP4F2</italic> expression in normal lung tissues and lung squamous cell carcinoma (LUSC) tissues under different subgroups from the TCGA database and analyzed them <italic>via</italic> UALCAN (<ext-link ext-link-type="uri" xlink:href="http://ualcan.path.uab.edu/index.html">http://ualcan.path.uab.edu/index.html</ext-link>), which is an interactive web resource for tumor subgroup gene expression analysis and survival analysis.</p>
</sec>
<sec id="s2_4">
<title>Statistical analysis</title>
<p>SPASS version 22.0 software was applied for statistical analysis. HWE was calculated for the control group by the chi-square test. Differences in the continuous characteristic (age) and categorical variable (gender) between patients with LC and controls were measured by the student&#x2019;s t-test and Pearson Chi-Square test, respectively. The correlation between <italic>CYP4F2</italic> variants and LC susceptibility was evaluated by logistic regression analysis adjusted for age and gender using PLINK software (version 1.07) under multiple genetic models (allele, genotype, dominant, recessive, and additive). Odds ratio (OR) and 95% confidence interval (CI) were calculated to assess the relationship between <italic>CYP4F2</italic> SNPs and LC risk (OR = 1: no impact; OR &lt;1: protective factor; OR &gt;1: risk factor). Finally, PLINK (version 1.07) and Haploview (version 4.2) softwares were used to analyze the pairwise linkage disequilibrium (LD) among five SNPs and generate an LD map to observe the linkage degree among them based on D&#x2019; and r-squared values. The SNPStats software (<ext-link ext-link-type="uri" xlink:href="https://www.snpstats.net/start.htm">https://www.snpstats.net/start.htm</ext-link>) was used to estimate the correlation between <italic>CYP4F2</italic> haplotypes and LC risk. In our study, the <italic>p</italic>-values of all tests were two-sided, and <italic>p &lt;</italic>0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Participant characteristics</title>
<p>The mean ages of 507 LC patients and 505 unrelated healthy controls were 61.30 &#xb1; 8.32 years and 58.91 &#xb1; 9.58 years, respectively (<xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>). In our study, there were no statistically significant differences in age (<italic>p</italic> = 0.525) and gender (<italic>p</italic> = 0.870) distribution between cases and controls.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Characteristics of patients with lung cancer and controls.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="left">Variables</th>
<th valign="middle" align="center">Cases (N = 507)</th>
<th valign="middle" align="center">Controls (N = 505)</th>
<th valign="middle" align="center">
<italic>p-</italic>value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="left">Age (mean &#xb1; SD), years</td>
<td valign="middle" align="center">61.30 &#xb1; 8.32</td>
<td valign="middle" align="center">58.91 &#xb1; 9.58</td>
<td valign="middle" align="center">0.525</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;&gt;60</td>
<td valign="middle" align="center">271 (53%)</td>
<td valign="middle" align="center">270 (53%)</td>
<td valign="middle" align="center">0.973</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;&#x2264;60</td>
<td valign="middle" align="center">236 (47%)</td>
<td valign="middle" align="center">235 (47%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">Sex</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.870</td>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Male</td>
<td valign="middle" align="center">353 (70%)</td>
<td valign="middle" align="center">354 (70%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Female</td>
<td valign="middle" align="center">154 (30%)</td>
<td valign="middle" align="center">151 (30%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">BMI (kg/m<sup>2</sup>)</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;&lt;24</td>
<td valign="middle" align="center">316 (62%)</td>
<td valign="middle" align="center">146 (29%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;&#x2265;24</td>
<td valign="middle" align="center">177 (35%)</td>
<td valign="middle" align="center">161 (32%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">14 (3%)</td>
<td valign="middle" align="center">198 (39%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Smoking status</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Yes</td>
<td valign="middle" align="center">251 (50%)</td>
<td valign="middle" align="center">136 (27%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;No</td>
<td valign="middle" align="center">250 (49%)</td>
<td valign="middle" align="center">140 (28%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">6 (1%)</td>
<td valign="middle" align="center">229 (45%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Drinking status</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Yes</td>
<td valign="middle" align="center">114 (22%)</td>
<td valign="middle" align="center">109 (22%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;No</td>
<td valign="middle" align="center">356 (70%)</td>
<td valign="middle" align="center">135 (27%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">27 (8%)</td>
<td valign="middle" align="center">261 (51%)</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Histology</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Adenocarcinoma</td>
<td valign="middle" align="center">187 (37%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Squamous</td>
<td valign="middle" align="center">119 (23%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">201 (40%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">LN metastasis</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Yes</td>
<td valign="middle" align="center">214 (42%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;No</td>
<td valign="middle" align="center">84 (17%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">209 (41%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<th valign="middle" colspan="4" align="left">Stage</th>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;I, II</td>
<td valign="middle" align="center">83 (16%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;III, IV</td>
<td valign="middle" align="center">260 (51%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="left">&#x2003;Absence</td>
<td valign="middle" align="center">164 (33%)</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>BMI, body mass index; LN, lymph node.</p>
</fn>
<fn>
<p>p &lt;0.05 indicates statistical significance.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_2">
<title>Basic information about the selected SNPs in <italic>CYP4F2</italic>
</title>
<p>The basic information about the five SNPs in <italic>CYP4F2</italic> (rs3093203, rs3093144, rs12459936, rs3093110, and rs3093193) among cases and controls was displayed (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>), including gene, SNP ID, position, alleles, HWE, and OR (95% CI). The five SNPs in controls were in accordance with HWE (<italic>p &gt;</italic>0.05). We further evaluated the association between the five SNPs and LC susceptibility by logistic regression (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). The four genetic models (genotype, dominant, recessive, and additive) were also applied to analyze the association by logistic regression adjusted for age and gender (<xref ref-type="supplementary-material" rid="SM1">
<bold>Table S1</bold>
</xref>). Unfortunately, there was no significant association between these five SNPs in <italic>CYP4F2</italic> and LC susceptibility under the allelic and genetic models.</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Basic information and allele frequencies of candidate SNPs in <italic>CYP4F2</italic>.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">SNP ID</th>
<th valign="middle" rowspan="2" align="center">Position</th>
<th valign="middle" align="center">Alleles</th>
<th valign="middle" rowspan="2" align="center">Role</th>
<th valign="middle" colspan="2" align="center">MAF</th>
<th valign="middle" rowspan="2" align="center">HWE <italic>p</italic>-value</th>
<th valign="middle" rowspan="2" align="center">OR (95% CI)</th>
<th valign="middle" rowspan="2" align="center">
<italic>p</italic>-value</th>
</tr>
<tr>
<th valign="middle" align="center">A/B</th>
<th valign="middle" align="center">Case</th>
<th valign="middle" align="center">Control</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">rs3093203</td>
<td valign="middle" align="center">Chr19:15878374</td>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="center">3&#x2019;UTR</td>
<td valign="middle" align="center">0.240</td>
<td valign="middle" align="center">0.229</td>
<td valign="middle" align="center">1.000</td>
<td valign="middle" align="center">1.06 (0.86&#x2013;1.31)</td>
<td valign="middle" align="center">0.558</td>
</tr>
<tr>
<td valign="middle" align="center">rs3093193</td>
<td valign="middle" align="center">Chr19:15881104</td>
<td valign="middle" align="center">C/G</td>
<td valign="middle" align="center">intronic</td>
<td valign="middle" align="center">0.288</td>
<td valign="middle" align="center">0.301</td>
<td valign="middle" align="center">0.525</td>
<td valign="middle" align="center">0.94 (0.78&#x2013;1.14)</td>
<td valign="middle" align="center">0.533</td>
</tr>
<tr>
<td valign="middle" align="center">rs12459936</td>
<td valign="middle" align="center">Chr19:15882231</td>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="center">intronic</td>
<td valign="middle" align="center">0.463</td>
<td valign="middle" align="center">0.450</td>
<td valign="middle" align="center">0.720</td>
<td valign="middle" align="center">1.05 (0.88&#x2013;1.26)</td>
<td valign="middle" align="center">0.557</td>
</tr>
<tr>
<td valign="middle" align="center">rs3093144</td>
<td valign="middle" align="center">Chr19:15891487</td>
<td valign="middle" align="center">A/G</td>
<td valign="middle" align="center">intronic</td>
<td valign="middle" align="center">0.187</td>
<td valign="middle" align="center">0.172</td>
<td valign="middle" align="center">0.755</td>
<td valign="middle" align="center">1.11 (0.88&#x2013;1.39)</td>
<td valign="middle" align="center">0.377</td>
</tr>
<tr>
<td valign="middle" align="center">rs3093110</td>
<td valign="middle" align="center">Chr19:15896974</td>
<td valign="middle" align="center">C/T</td>
<td valign="middle" align="center">intronic</td>
<td valign="middle" align="center">0.105</td>
<td valign="middle" align="center">0.129</td>
<td valign="middle" align="center">0.694</td>
<td valign="middle" align="center">0.79 (0.06&#x2013;1.03)</td>
<td valign="middle" align="center">0.084</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SNP, single-nucleotide polymorphism; Chr, chromosome; MAF, minor allele frequency; HWE, Hardy&#x2013;Weinberg equilibrium; OR, odds ratio; 95% CI, 95% confidence interval.</p>
</fn>
<fn>
<p>A/B: minor/major allele in the controls; ORs (95% CI) were calculated by logistic regression; p-values were calculated by Pearson &#x3c7;<sup>2</sup> test.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_3">
<title>Stratification analysis by smoking status</title>
<p>The smoking-stratified analysis (<xref ref-type="table" rid="T3">
<bold>Table&#xa0;3</bold>
</xref>) was performed to examine the relationship between <italic>CYP4F2</italic> variants and LC risk. Our results showed that rs12459936 in <italic>CYP4F2</italic> was associated with an increased risk of LC in no-smoking individuals under the allele (T <italic>vs</italic>. C: OR = 1.38, 95% CI: 1.02&#x2013;1.85, <italic>p</italic> = 0.035), genotype (TT <italic>vs</italic>. CC: OR = 2.00, 95% CI: 1.05&#x2013;3.82, <italic>p</italic> = 0.035), and additive (OR = 1.40, 95% CI: 1.03&#x2013;1.92, <italic>p</italic> = 0.034) models. On the contrary, rs3093110 was found to have a protective effect against LC risk in no-smoking individuals under the genotype (GA <italic>vs</italic>. AA: OR = 0.56, 95% CI: 0.33&#x2013;0.94, <italic>p</italic> = 0.027) and dominant (GG + GA <italic>vs</italic>. AA: OR = 0.58, 95% CI: 0.35&#x2013;0.96, <italic>p</italic> = 0.035) models.</p>
<table-wrap id="T3" position="float">
<label>Table&#xa0;3</label>
<caption>
<p>The association of variants in <italic>CYP4F2</italic> with lung cancer susceptibility stratified by smoking status.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">SNP ID</th>
<th valign="middle" rowspan="2" align="center">Models</th>
<th valign="middle" rowspan="2" align="center">Genotypes</th>
<th valign="middle" colspan="4" align="center">No smoking</th>
<th valign="middle" colspan="4" align="center">Smoking</th>
</tr>
<tr>
<th valign="middle" align="center">Cases (%)</th>
<th valign="middle" align="center">Controls (%)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
<th valign="middle" align="center">Cases (%)</th>
<th valign="middle" align="center">Controls (%)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="10" align="left">rs12459936</td>
<td valign="middle" align="center">Allele</td>
<td valign="bottom" align="left">C</td>
<td valign="bottom" align="center">259 (51.8%)</td>
<td valign="bottom" align="center">167 (59.6%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="bottom" align="center">279 (55.6%)</td>
<td valign="bottom" align="center">151 (55.5%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="bottom" align="left">T</td>
<td valign="bottom" align="center">241 (48.2%)</td>
<td valign="bottom" align="center">113 (40.4%)</td>
<td valign="middle" align="center">1.38 (1.02&#x2013;1.85)</td>
<td valign="middle" align="center">
<bold>0.035</bold>
</td>
<td valign="bottom" align="center">223 (44.4%)</td>
<td valign="bottom" align="center">121 (44.5%)</td>
<td valign="middle" align="center">1.00 (0.74&#x2013;1.34)</td>
<td valign="middle" align="center">0.987</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="left">CC</td>
<td valign="middle" align="center">63 (25.2%)</td>
<td valign="middle" align="center">47 (33.6%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">83 (33.1%)</td>
<td valign="middle" align="center">45 (33.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">TT</td>
<td valign="middle" align="center">54 (21.6%)</td>
<td valign="middle" align="center">20 (14.3%)</td>
<td valign="middle" align="center">2.00 (1.05&#x2013;3.82)</td>
<td valign="middle" align="center">
<bold>0.035</bold>
</td>
<td valign="middle" align="center">55 (21.9%)</td>
<td valign="middle" align="center">30 (22.1%)</td>
<td valign="middle" align="center">1.00 (0.56&#x2013;1.77)</td>
<td valign="middle" align="center">0.993</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">TC</td>
<td valign="middle" align="center">133 (53.2%)</td>
<td valign="middle" align="center">73 (52.1%)</td>
<td valign="middle" align="center">1.35 (0.83&#x2013;2.18)</td>
<td valign="middle" align="center">0.223</td>
<td valign="middle" align="center">113 (45.0%)</td>
<td valign="middle" align="center">61 (44.9%)</td>
<td valign="middle" align="center">1.00 (0.62&#x2013;1.62)</td>
<td valign="middle" align="center">0.989</td>
</tr>
<tr>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="left">CC</td>
<td valign="middle" align="center">63 (25.2%)</td>
<td valign="middle" align="center">47 (33.6%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">83 (33.1%)</td>
<td valign="middle" align="center">45 (33.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">TT + TC</td>
<td valign="middle" align="center">187 (74.8%)</td>
<td valign="middle" align="center">93 (66.4%)</td>
<td valign="middle" align="center">1.49 (0.94&#x2013;2.35)</td>
<td valign="middle" align="center">0.090</td>
<td valign="middle" align="center">168 (66.9%)</td>
<td valign="middle" align="center">91 (66.9%)</td>
<td valign="middle" align="center">1.00 (0.64&#x2013;1.56)</td>
<td valign="middle" align="center">0.995</td>
</tr>
<tr>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="left">TC + CC</td>
<td valign="middle" align="center">196 (78.4%)</td>
<td valign="middle" align="center">120 (85.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">196 (78.1%)</td>
<td valign="middle" align="center">106 (77.9%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="left">TT</td>
<td valign="middle" align="center">54 (21.6%)</td>
<td valign="middle" align="center">20 (14.3%)</td>
<td valign="middle" align="center">1.65 (0.94&#x2013;2.91)</td>
<td valign="middle" align="center">0.084</td>
<td valign="middle" align="center">55 (21.9%)</td>
<td valign="middle" align="center">30 (22.1%)</td>
<td valign="middle" align="center">1.00 (0.60&#x2013;1.65)</td>
<td valign="middle" align="center">0.986</td>
</tr>
<tr>
<td valign="middle" align="center">Additive</td>
<td valign="middle" align="left">TT + TC + CC</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.40 (1.03&#x2013;1.92)</td>
<td valign="middle" align="center">
<bold>0.034</bold>
</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.00 (0.75&#x2013;1.33)</td>
<td valign="middle" align="center">0.996</td>
</tr>
<tr>
<td valign="middle" rowspan="10" align="left">rs3093110</td>
<td valign="middle" align="center">Allele</td>
<td valign="bottom" align="left">A</td>
<td valign="bottom" align="center">453 (90.6%)</td>
<td valign="bottom" align="center">243 (86.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="bottom" align="center">445 (88.6%)</td>
<td valign="bottom" align="center">244 (90.4%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="bottom" align="left">G</td>
<td valign="bottom" align="center">47 (9.4%)</td>
<td valign="bottom" align="center">37 (13.2%)</td>
<td valign="middle" align="center">0.68 (0.43&#x2013;1.08)</td>
<td valign="middle" align="center">0.099</td>
<td valign="bottom" align="center">57 (11.4%)</td>
<td valign="bottom" align="center">26 (9.6%)</td>
<td valign="middle" align="center">1.20 (0.74&#x2013;1.96)</td>
<td valign="middle" align="center">0.461</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="left">AA</td>
<td valign="middle" align="center">206 (82.4%)</td>
<td valign="middle" align="center">104 (74.3%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">198 (78.9%)</td>
<td valign="middle" align="center">111 (82.2%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center">0.719</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">GG</td>
<td valign="middle" align="center">3 (1.2%)</td>
<td valign="middle" align="center">1 (0.7%)</td>
<td valign="middle" align="center">1.26 (0.13&#x2013;12.41)</td>
<td valign="middle" align="center">0.844</td>
<td valign="middle" align="center">4 (1.6%)</td>
<td valign="middle" align="center">2 (1.5%)</td>
<td valign="middle" align="center">1.09 (0.20&#x2013;6.04)</td>
<td valign="middle" align="center">0.923</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">GA</td>
<td valign="middle" align="center">41 (16.4%)</td>
<td valign="middle" align="center">35 (25.0%)</td>
<td valign="middle" align="center">0.56 (0.33&#x2013;0.94)</td>
<td valign="middle" align="center">
<bold>0.027</bold>
</td>
<td valign="middle" align="center">49 (19.5%)</td>
<td valign="middle" align="center">22 (16.3%)</td>
<td valign="middle" align="center">1.26 (0.72&#x2013;2.20)</td>
<td valign="middle" align="center">0.417</td>
</tr>
<tr>
<td valign="middle" align="left">Dominant</td>
<td valign="middle" align="left">AA</td>
<td valign="middle" align="center">206 (82.4%)</td>
<td valign="middle" align="center">104 (74.3%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">198 (78.9%)</td>
<td valign="middle" align="center">111 (82.2%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="left">GG + GA</td>
<td valign="middle" align="center">44 (17.6%)</td>
<td valign="middle" align="center">36 (25.7%)</td>
<td valign="middle" align="center">0.58 (0.35&#x2013;0.96)</td>
<td valign="middle" align="center">
<bold>0.035</bold>
</td>
<td valign="middle" align="center">53 (21.1%)</td>
<td valign="middle" align="center">24 (17.8%)</td>
<td valign="middle" align="center">1.25 (0.73&#x2013;2.13)</td>
<td valign="middle" align="center">0.425</td>
</tr>
<tr>
<td valign="middle" align="left">Recessive</td>
<td valign="middle" align="left">GA + AA</td>
<td valign="middle" align="center">247 (98.8%)</td>
<td valign="middle" align="center">139 (99.3%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">247 (98.4%)</td>
<td valign="middle" align="center">133 (98.5%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="left">GG</td>
<td valign="middle" align="center">3 (1.2%)</td>
<td valign="middle" align="center">1 (0.7%)</td>
<td valign="middle" align="center">1.44 (0.15&#x2013;14.16)</td>
<td valign="middle" align="center">0.755</td>
<td valign="middle" align="center">4 (1.6%)</td>
<td valign="middle" align="center">2 (1.5%)</td>
<td valign="middle" align="center">1.04 (0.19&#x2013;5.78)</td>
<td valign="middle" align="center">0.961</td>
</tr>
<tr>
<td valign="middle" align="left">Additive</td>
<td valign="middle" align="left">GG + GA + AA</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.63 (0.40&#x2013;1.02)</td>
<td valign="middle" align="center">0.590</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.20 (0.74&#x2013;1.94)</td>
<td valign="middle" align="center">0.468</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SNP, single-nucleotide polymorphism; BMI, body mass index; OR, odds ratio; 95% CI, 95% confidence interval.</p>
</fn>
<fn>
<p>Bold values are statistically significant; OR (95% CI) and p-values were computed by logistic regression analysis with adjustments for age and gender.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_4">
<title>Stratification analysis by gender</title>
<p>In addition, the analysis stratified by gender (<xref ref-type="table" rid="T4">
<bold>Table&#xa0;4</bold>
</xref>) demonstrated that rs3093193 (G <italic>vs</italic>. C: OR = 0.66, 95% CI: 0.47&#x2013;0.92, <italic>p</italic> = 0.016; GG <italic>vs</italic>. CC: OR = 0.33, 95% CI: 0.14&#x2013;0.77, <italic>p</italic> = 0.011; GG <italic>vs</italic>. GC + CC: OR = 0.38, 95% CI: 0.17&#x2013;0.86, <italic>p</italic> = 0.021; additive: OR = 0.64, 95% CI: 0.45&#x2013;0.91, <italic>p</italic> = 0.014) was related to a decreased risk of LC in females. Rs3093144 in the recessive model (TT <italic>vs</italic>. TC + CC: OR = 0.20, 95% CI: 0.04&#x2013;0.96, <italic>p</italic> = 0.045) and rs3093110 in the allele, genotype, dominant, and additive models (G <italic>vs</italic>. A: OR = 0.54, 95% CI: 0.33&#x2013;0.87, <italic>p</italic> = 0.010; GA <italic>vs</italic>. AA: OR = 0.50, 95% CI: 0.29&#x2013;0.87, <italic>p</italic> = 0.014; GG + GA <italic>vs</italic>. AA: OR = 0.49, 95% CI: 0.29&#x2013;0.84, <italic>p</italic> = 0.010; additive: OR = 0.54, 95% CI: 0.33&#x2013;0.87, <italic>p</italic> = 0.011) showed a protective effect on LC in females. However, the <italic>CYP4F2</italic> rs12459936 was associated with an increased risk of LC in females under the allele, genotype, dominant, and additive models (T <italic>vs</italic>. C: OR = 1.64, 95% CI: 1.19&#x2013;2.27, <italic>p</italic> = 0.002; TT <italic>vs</italic>.CC: OR = 2.57, 95% CI: 1.49&#x2013;4.39, <italic>p</italic> = 0.006; TC <italic>vs</italic>.CC: OR = 2.56, 95% CI: 1.49&#x2013;4.39, <italic>p</italic> = 0.001; TT + TC <italic>vs</italic>.CC: OR = 2.56, 95% CI: 1.53&#x2013;4.28, <italic>p &lt;</italic>0.001; additive: OR = 1.67, 95% CI: 1.20&#x2013;2.33, <italic>p</italic> = 0.002).</p>
<table-wrap id="T4" position="float">
<label>Table&#xa0;4</label>
<caption>
<p>The association of variants in <italic>CYP4F2</italic> with lung cancer susceptibility stratified by gender.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="left">SNP ID</th>
<th valign="middle" rowspan="2" align="center">Models</th>
<th valign="middle" rowspan="2" align="center">Genotypes</th>
<th valign="middle" colspan="4" align="center">Males</th>
<th valign="middle" colspan="4" align="center">Females</th>
</tr>
<tr>
<th valign="middle" align="center">Cases (%)</th>
<th valign="middle" align="center">Controls (%)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
<th valign="middle" align="center">Cases (%)</th>
<th valign="middle" align="center">Controls (%)</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="10" align="left">rs3093193</td>
<td valign="bottom" align="left">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="center">500 (70.8%)</td>
<td valign="middle" align="center">515 (72.9%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">222 (72.1%)</td>
<td valign="middle" align="center">190 (62.9%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">G</td>
<td valign="middle" align="center">206 (29.2%)</td>
<td valign="middle" align="center">191 (27.1%)</td>
<td valign="middle" align="center">1.11 (0.88&#x2013;1.40)</td>
<td valign="middle" align="center">0.375</td>
<td valign="middle" align="center">86 (27.9%)</td>
<td valign="middle" align="center">112 (37.1%)</td>
<td valign="middle" align="center">0.66 (0.47&#x2013;0.92)</td>
<td valign="middle" align="center">
<bold>0.016</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">179 (50.7%)</td>
<td valign="middle" align="center">183 (51.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">77 (50.0%)</td>
<td valign="middle" align="center">60 (39.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">GG</td>
<td valign="middle" align="center">32 (9.1%)</td>
<td valign="middle" align="center">21 (5.9%)</td>
<td valign="middle" align="center">1.59 (0.88&#x2013;2.87)</td>
<td valign="middle" align="center">0.124</td>
<td valign="middle" align="center">9 (5.8%)</td>
<td valign="middle" align="center">21 (13.9%)</td>
<td valign="middle" align="center">0.33 (0.14&#x2013;0.77)</td>
<td valign="middle" align="center">
<bold>0.011</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GC</td>
<td valign="middle" align="center">142 (40.2%)</td>
<td valign="middle" align="center">149 (42.2%)</td>
<td valign="middle" align="center">0.98 (0.72&#x2013;1.34)</td>
<td valign="middle" align="center">0.911</td>
<td valign="middle" align="center">68 (44.2%)</td>
<td valign="middle" align="center">70 (46.4%)</td>
<td valign="middle" align="center">0.75 (0.47&#x2013;1.21)</td>
<td valign="middle" align="center">0.243</td>
</tr>
<tr>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">179 (50.7%)</td>
<td valign="middle" align="center">183 (51.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">77 (50.0%)</td>
<td valign="middle" align="center">60 (39.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GG + GC</td>
<td valign="middle" align="center">174 (49.3%)</td>
<td valign="middle" align="center">170 (48.2%)</td>
<td valign="middle" align="center">1.06 (0.79&#x2013;1.42)</td>
<td valign="middle" align="center">0.717</td>
<td valign="middle" align="center">77 (50.0%)</td>
<td valign="middle" align="center">91 (60.3%)</td>
<td valign="middle" align="center">0.66 (0.42&#x2013;1.04)</td>
<td valign="middle" align="center">0.071</td>
</tr>
<tr>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">GC + CC</td>
<td valign="middle" align="center">321 (90.9%)</td>
<td valign="middle" align="center">332 (94.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">145 (94.2%)</td>
<td valign="middle" align="center">130 (86.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">GG</td>
<td valign="middle" align="center">32 (9.1%)</td>
<td valign="middle" align="center">21 (5.9%)</td>
<td valign="middle" align="center">1.60 (0.90&#x2013;2.84)</td>
<td valign="middle" align="center">0.107</td>
<td valign="middle" align="center">9 (5.8%)</td>
<td valign="middle" align="center">21 (13.9%)</td>
<td valign="middle" align="center">0.38 (0.17&#x2013;0.86)</td>
<td valign="middle" align="center">
<bold>0.021</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Additive</td>
<td valign="middle" align="center">GG + GC + CC</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.12 (0.89&#x2013;1.42)</td>
<td valign="middle" align="center">0.333</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.64 (0.45&#x2013;0.91)</td>
<td valign="middle" align="center">
<bold>0.014</bold>
</td>
</tr>
<tr>
<td valign="middle" rowspan="10" align="left">rs12459936</td>
<td valign="bottom" align="left">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="center">395 (55.9%)</td>
<td valign="middle" align="center">372 (52.5%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">150 (48.7%)</td>
<td valign="middle" align="center">184 (60.9%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="center">311 (44.1%)</td>
<td valign="middle" align="center">336 (47.5%)</td>
<td valign="middle" align="center">0.87 (0.71&#x2013;1.08)</td>
<td valign="middle" align="center">0.199</td>
<td valign="middle" align="center">158 (51.3%)</td>
<td valign="middle" align="center">118 (39.1%)</td>
<td valign="middle" align="center">1.64 (1.19&#x2013;2.27)</td>
<td valign="middle" align="center">
<bold>0.002</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">116 (32.9%)</td>
<td valign="middle" align="center">95 (26.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">32 (20.8%)</td>
<td valign="middle" align="center">60 (39.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TT</td>
<td valign="middle" align="center">74 (21.0%)</td>
<td valign="middle" align="center">77 (21.8%)</td>
<td valign="middle" align="center">0.79 (0.52&#x2013;1.00)</td>
<td valign="middle" align="center">0.269</td>
<td valign="middle" align="center">36 (23.4%)</td>
<td valign="middle" align="center">27 (17.9%)</td>
<td valign="middle" align="center">2.57 (1.32&#x2013;5.00)</td>
<td valign="middle" align="center">
<bold>0.006</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TC</td>
<td valign="middle" align="center">163 (46.2%)</td>
<td valign="middle" align="center">182 (51.4%)</td>
<td valign="middle" align="center">0.74 (0.52&#x2013;1.05)</td>
<td valign="middle" align="center">0.088</td>
<td valign="middle" align="center">86 (55.8%)</td>
<td valign="middle" align="center">64 (42.4%)</td>
<td valign="middle" align="center">2.56 (1.49&#x2013;4.39)</td>
<td valign="middle" align="center">
<bold>0.001</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">116 (32.9%)</td>
<td valign="middle" align="center">95 (26.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">32 (20.8%)</td>
<td valign="middle" align="center">60 (39.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TT + TC</td>
<td valign="middle" align="center">237 (67.1%)</td>
<td valign="middle" align="center">259 (73.2%)</td>
<td valign="middle" align="center">0.76 (0.55&#x2013;1.05)</td>
<td valign="middle" align="center">0.090</td>
<td valign="middle" align="center">122 (79.2%)</td>
<td valign="middle" align="center">91 (60.3%)</td>
<td valign="middle" align="center">2.56 (1.53&#x2013;4.28)</td>
<td valign="middle" align="center">
<bold>&lt;0.001</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">TC + CC</td>
<td valign="middle" align="center">279 (79.0%)</td>
<td valign="middle" align="center">277 (78.2%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">118 (76.6%)</td>
<td valign="middle" align="center">130 (82.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">TT</td>
<td valign="middle" align="center">74 (21.0%)</td>
<td valign="middle" align="center">77 (21.8%)</td>
<td valign="middle" align="center">0.95 (0.66&#x2013;1.36)</td>
<td valign="middle" align="center">0.784</td>
<td valign="middle" align="center">36 (23.4%)</td>
<td valign="middle" align="center">27 (17.9%)</td>
<td valign="middle" align="center">1.41 (0.80&#x2013;2.47)</td>
<td valign="middle" align="center">0.233</td>
</tr>
<tr>
<td valign="middle" align="center">Additive</td>
<td valign="middle" align="center">TT + TC + CC</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.88 (0.71&#x2013;1.08)</td>
<td valign="middle" align="center">0.212</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.67 (1.20&#x2013;2.33)</td>
<td valign="middle" align="center">
<bold>0.002</bold>
</td>
</tr>
<tr>
<td valign="middle" rowspan="10" align="left">rs3093144</td>
<td valign="bottom" align="left">Allele</td>
<td valign="middle" align="center">C</td>
<td valign="middle" align="center">571 (80.9%)</td>
<td valign="middle" align="center">595 (84%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">253 (82.1%)</td>
<td valign="middle" align="center">241 (79.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">T</td>
<td valign="middle" align="center">135 (19.1%)</td>
<td valign="middle" align="center">113 (16%)</td>
<td valign="middle" align="center">1.25 (0.95&#x2013;1.64)</td>
<td valign="middle" align="center">0.118</td>
<td valign="middle" align="center">55 (17.9%)</td>
<td valign="middle" align="center">61 (20.2%)</td>
<td valign="middle" align="center">0.86 (0.57&#x2013;1.29)</td>
<td valign="middle" align="center">0.461</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">232 (65.7%)</td>
<td valign="middle" align="center">248 (70.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">101 (65.6%)</td>
<td valign="middle" align="center">99 (65.6%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TT</td>
<td valign="middle" align="center">14 (4.0%)</td>
<td valign="middle" align="center">7 (2.0%)</td>
<td valign="middle" align="center">2.15 (0.85&#x2013;5.43)</td>
<td valign="middle" align="center">0.105</td>
<td valign="middle" align="center">2 (1.3%)</td>
<td valign="middle" align="center">9 (6.0%)</td>
<td valign="middle" align="center">0.21 (0.04&#x2013;1.02)</td>
<td valign="middle" align="center">0.053</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TC</td>
<td valign="middle" align="center">107 (30.3%)</td>
<td valign="middle" align="center">99 (28.0%)</td>
<td valign="middle" align="center">1.16 (0.84&#x2013;1.61)</td>
<td valign="middle" align="center">0.370</td>
<td valign="middle" align="center">51 (33.1%)</td>
<td valign="middle" align="center">43 (28.5%)</td>
<td valign="middle" align="center">1.16 (0.71&#x2013;1.90)</td>
<td valign="middle" align="center">0.549</td>
</tr>
<tr>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">CC</td>
<td valign="middle" align="center">232 (65.7%)</td>
<td valign="middle" align="center">248 (70.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">101 (65.6%)</td>
<td valign="middle" align="center">99 (65.6%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">TT + TC</td>
<td valign="middle" align="center">121 (34.3%)</td>
<td valign="middle" align="center">106 (29.9%)</td>
<td valign="middle" align="center">1.23 (0.89&#x2013;1.68)</td>
<td valign="middle" align="center">0.206</td>
<td valign="middle" align="center">53 (34.4%)</td>
<td valign="middle" align="center">52 (34.4%)</td>
<td valign="middle" align="center">1.00 (0.62&#x2013;1.60)</td>
<td valign="middle" align="center">0.997</td>
</tr>
<tr>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">TC + CC</td>
<td valign="middle" align="center">339 (96.0%)</td>
<td valign="middle" align="center">347 (98.0%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">152 (98.7%)</td>
<td valign="middle" align="center">142 (94.0%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">TT</td>
<td valign="middle" align="center">14 (4.0%)</td>
<td valign="middle" align="center">7 (2.0%)</td>
<td valign="middle" align="center">2.06 (0.82&#x2013;5.16)</td>
<td valign="middle" align="center">0.125</td>
<td valign="middle" align="center">2 (1.3%)</td>
<td valign="middle" align="center">9 (6.0%)</td>
<td valign="middle" align="center">0.20 (0.04&#x2013;0.96)</td>
<td valign="middle" align="center">
<bold>0.045</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Additive</td>
<td valign="middle" align="center">TT + TC + CC</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">1.25 (0.95&#x2013;1.65)</td>
<td valign="middle" align="center">0.110</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.86 (0.57&#x2013;1.29)</td>
<td valign="middle" align="center">0.460</td>
</tr>
<tr>
<td valign="middle" rowspan="10" align="left">rs3093110</td>
<td valign="bottom" align="left">Allele</td>
<td valign="middle" align="center">A</td>
<td valign="middle" align="center">631 (89.4%)</td>
<td valign="middle" align="center">626 (88.9%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">277 (89.9%)</td>
<td valign="middle" align="center">250 (82.8%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">G</td>
<td valign="middle" align="center">75 (10.6%)</td>
<td valign="middle" align="center">78 (11.1%)</td>
<td valign="middle" align="center">0.95 (0.68&#x2013;1.33)</td>
<td valign="middle" align="center">0.783</td>
<td valign="middle" align="center">31 (10.1%)</td>
<td valign="middle" align="center">52 (17.2%)</td>
<td valign="middle" align="center">0.54 (0.33&#x2013;0.87)</td>
<td valign="middle" align="center">
<bold>0.010</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Genotype</td>
<td valign="middle" align="center">AA</td>
<td valign="middle" align="center">283 (80.2%)</td>
<td valign="middle" align="center">277 (78.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">125 (81.2%)</td>
<td valign="middle" align="center">103 (68.2%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GG</td>
<td valign="middle" align="center">5 (1.4%)</td>
<td valign="middle" align="center">3 (0.9%)</td>
<td valign="middle" align="center">1.70 (0.40&#x2013;7.19)</td>
<td valign="middle" align="center">0.474</td>
<td valign="middle" align="center">2 (1.3%)</td>
<td valign="middle" align="center">4 (2.6%)</td>
<td valign="middle" align="center">0.42 (0.07&#x2013;2.32)</td>
<td valign="middle" align="center">0.318</td>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GA</td>
<td valign="middle" align="center">65 (18.4%)</td>
<td valign="middle" align="center">72 (20.5%)</td>
<td valign="middle" align="center">0.89 (0.61&#x2013;1.30)</td>
<td valign="middle" align="center">0.549</td>
<td valign="middle" align="center">27 (17.5%)</td>
<td valign="middle" align="center">44 (29.1%)</td>
<td valign="middle" align="center">0.50 (0.29&#x2013;0.87)</td>
<td valign="middle" align="center">
<bold>0.014</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Dominant</td>
<td valign="middle" align="center">AA</td>
<td valign="middle" align="center">283 (80.2%)</td>
<td valign="middle" align="center">277 (78.7%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">125 (81.2%)</td>
<td valign="middle" align="center">103 (68.2%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center"/>
<td valign="middle" align="center">GG + GA</td>
<td valign="middle" align="center">70 (19.8%)</td>
<td valign="middle" align="center">75 (21.3%)</td>
<td valign="middle" align="center">0.92 (0.64&#x2013;1.33)</td>
<td valign="middle" align="center">0.668</td>
<td valign="middle" align="center">29 (18.8%)</td>
<td valign="middle" align="center">48 (31.8%)</td>
<td valign="middle" align="center">0.49 (0.29&#x2013;0.84)</td>
<td valign="middle" align="center">
<bold>0.010</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">Recessive</td>
<td valign="middle" align="center">GA + AA</td>
<td valign="middle" align="center">348 (98.6%)</td>
<td valign="middle" align="center">349 (99.1%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">152 (98.7%)</td>
<td valign="middle" align="center">147 (97.4%)</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="bottom" align="center"/>
<td valign="middle" align="center">GG</td>
<td valign="middle" align="center">5 (1.4%)</td>
<td valign="middle" align="center">3 (0.9%)</td>
<td valign="middle" align="center">1.74 (0.41&#x2013;7.35)</td>
<td valign="middle" align="center">0.454</td>
<td valign="middle" align="center">2 (1.3%)</td>
<td valign="middle" align="center">4 (2.6%)</td>
<td valign="middle" align="center">0.48 (0.09&#x2013;2.68)</td>
<td valign="middle" align="center">0.405</td>
</tr>
<tr>
<td valign="middle" align="center">Additive</td>
<td valign="middle" align="center">GG + GA + AA</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.96 (0.69&#x2013;1.35)</td>
<td valign="middle" align="center">0.832</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">&#x2013;</td>
<td valign="middle" align="center">0.54 (0.33&#x2013;0.87)</td>
<td valign="middle" align="center">
<bold>0.011</bold>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Bold values are statistically significant; p-values were computed by logistic regression analysis with adjustment for age.</p>
</fn>
<fn>
<p>SNP, single-nucleotide polymorphism; BMI, body mass index; OR, odds ratio; 95% CI, 95% confidence interval.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_5">
<title>Haplotype analysis</title>
<p>Finally, the results of haplotype analysis indicated a strong 18-kb LD block among the five SNPs (rs3093203, rs3093193, rs12459936, rs3093144, and rs3093110) (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref> and <xref ref-type="supplementary-material" rid="SM1">
<bold>Table S2</bold>
</xref>). Compared with haplotype &#x201c;GCTCA,&#x201d; haplotypes &#x201c;GGCTA&#x201d; (OR = 0.63, 95% CI: 0.40&#x2013;1.00, <italic>p</italic> = 0.048) and &#x201c;GGCCG&#x201d; (OR = 0.46, 95% CI: 0.27&#x2013;0.78, <italic>p</italic> = 0.004) were associated with a decreased risk of LC in females (<xref ref-type="table" rid="T5">
<bold>Table&#xa0;5</bold>
</xref>). For non-smokers, the haplotype &#x201c;GGCCG&#x201d; (OR = 0.54, 95% CI: 0.32&#x2013;0.90, <italic>p</italic> = 0.046) was also associated with decreased susceptibility to LC (<xref ref-type="table" rid="T5">
<bold>Table&#xa0;5</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Haplotype block map for SNPs in the <italic>CYP4F2</italic> gene. The numbers inside the diamonds indicate the D&#x2032; value &#xd7; 100 for pairwise analyses.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1114218-g001.tif"/>
</fig>
<table-wrap id="T5" position="float">
<label>Table&#xa0;5</label>
<caption>
<p>The frequency of <italic>CYP4F2</italic> haplotypes and their association with the risk of lung cancer in subgroups.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" rowspan="2" align="center">SNP ID</th>
<th valign="middle" rowspan="2" align="center">Haplotypes</th>
<th valign="middle" colspan="4" align="center">Female</th>
<th valign="middle" colspan="4" align="center">No-smoker</th>
</tr>
<tr>
<th valign="top" align="center">Controls-Fre</th>
<th valign="top" align="center">Cases-Fre</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
<th valign="top" align="center">Controls-Fre</th>
<th valign="top" align="center">Cases-Fre</th>
<th valign="middle" align="center">OR (95% CI)</th>
<th valign="middle" align="center">
<italic>p</italic>-value</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" rowspan="5" align="center">rs3093203|rs3093193|rs12459936|rs3093144|rs3093110</td>
<td valign="middle" align="center">GCTCA</td>
<td valign="middle" align="center">0.387</td>
<td valign="middle" align="center">0.509</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
<td valign="middle" align="center">0.404</td>
<td valign="middle" align="center">0.479</td>
<td valign="middle" align="center">1</td>
<td valign="middle" align="center"/>
</tr>
<tr>
<td valign="middle" align="center">ACCCA</td>
<td valign="middle" align="center">0.211</td>
<td valign="middle" align="center">0.184</td>
<td valign="middle" align="center">0.64 (0.40&#x2013;1.02)</td>
<td valign="middle" align="center">0.060</td>
<td valign="middle" align="center">0.246</td>
<td valign="middle" align="center">0.219</td>
<td valign="middle" align="center">0.79 (0.53&#x2013;1.17)</td>
<td valign="middle" align="center">0.250</td>
</tr>
<tr>
<td valign="middle" align="center">GGCTA</td>
<td valign="middle" align="center">0.199</td>
<td valign="middle" align="center">0.166</td>
<td valign="middle" align="center">0.63 (0.40&#x2013;1.00)</td>
<td valign="middle" align="center">
<bold>0.048</bold>
</td>
<td valign="middle" align="center">0.182</td>
<td valign="middle" align="center">0.168</td>
<td valign="middle" align="center">0.72 (0.47&#x2013;1.12)</td>
<td valign="middle" align="center">0.140</td>
</tr>
<tr>
<td valign="middle" align="center">GGCCG</td>
<td valign="middle" align="center">0.162</td>
<td valign="middle" align="center">0.101</td>
<td valign="middle" align="center">0.46 (0.27&#x2013;0.78)</td>
<td valign="middle" align="center">
<bold>0.004</bold>
</td>
<td valign="middle" align="center">0.132</td>
<td valign="middle" align="center">0.092</td>
<td valign="middle" align="center">0.54 (0.32&#x2013;0.90)</td>
<td valign="middle" align="center">
<bold>0.019</bold>
</td>
</tr>
<tr>
<td valign="middle" align="center">GCCCA</td>
<td valign="middle" align="center">0.031</td>
<td valign="middle" align="center">0.020</td>
<td valign="middle" align="center">0.50 (0.17&#x2013;1.52)</td>
<td valign="middle" align="center">0.220</td>
<td valign="middle" align="center">0.029</td>
<td valign="middle" align="center">0.021</td>
<td valign="middle" align="center">0.57 (0.21&#x2013;1.56)</td>
<td valign="middle" align="center">0.280</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>SNP, single nucleotide polymorphism, OR, odds ratio, CI, confidence interval.</p>
</fn>
<fn>
<p>p &lt;0.05 indicates statistical significance. Significant values are marked in bold.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3_6">
<title>Bioinformatics analysis of <italic>CYP4F2</italic> expression in LC</title>
<p>The analysis of the expression level of <italic>CYF4F2</italic> in normal and LUSC tissues and its effect on the survival of these patients was conducted using UALCAN online analysis software based on the TCGA database, as shown in <xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>. We observed that the expression level of <italic>CYP4F2</italic> was significantly different between normal and LUSC tissues (<italic>p &lt;</italic>0.001). In addition, the expression level of <italic>CYP4F2</italic> was higher in non-smoking LUSC patients than in normal and smoking ones (<italic>p &lt;</italic>0.001). The expression level was higher in males than in females (<italic>p &lt;</italic>0.001). Moreover, a high expression level of <italic>CYP4F2</italic> was found to be significantly related to the poor prognosis of non-smoking LUSC patients (<italic>p</italic> = 0.033).</p>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>The expression of <italic>CYP4F2</italic> in normal lung squamous cell carcinoma tissues based on different types.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fonc-13-1114218-g002.tif"/>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>In our study, the connection between five variants in <italic>CYP4F2</italic> and LC risk in the Chinese Han population was detected. Association analyses revealed that <italic>CYP4F2</italic> rs12459936 increased susceptibility to LC in non-smoking individuals and females. In contrast, rs3093110 showed a protective effect on LC susceptibility in non-smoking groups and females. The two SNPs (rs3093193 and rs3093144) were also associated with a decreased risk of LC in females.</p>
<p>The <italic>CYP4F2</italic> gene, a member of the CYP450 superfamily, located on chromosome 19p13.12, has been shown to be expressed at higher levels in certain types of cancerous tissues, such as the thyroid, ovarian, breast, and colon (<xref ref-type="bibr" rid="B10">10</xref>). Eun et&#xa0;al. have confirmed that low expression of <italic>CYP4F2</italic> may contribute to the progression of hepatocellular carcinoma (HCC) and decrease survival rates due to its involvement in various metabolic pathways (<xref ref-type="bibr" rid="B15">15</xref>). A similar study showed that <italic>CYP4F2</italic> expression was higher in pancreatic ductal adenocarcinoma (PDA) patients than in normal ones and negatively correlated with age (<xref ref-type="bibr" rid="B16">16</xref>). Database prediction found that <italic>CYP4F2</italic> was highly expressed in lung cancer tissues. The expression of <italic>CYP4F2</italic> was higher in men than women and higher in non-smokers than smokers. Additionally, Xu et&#xa0;al. have reported that <italic>CYP4F</italic> generates 20-HETE by catalyzing &#x3c9;-hydroxylation of arachidonic acid (<xref ref-type="bibr" rid="B17">17</xref>). According to previous studies, 20-HETE plays a significant role in tumor progression. Colombero et&#xa0;al. have demonstrated that HET0016, a selective inhibitor of 20-HETE synthesis, can reduce the proliferation of prostate cancer (<xref ref-type="bibr" rid="B18">18</xref>), while another study has revealed that the antagonist of 20-HETE, WIT002, is able to inhibit tumor growth in a renal cell carcinoma cell line (<xref ref-type="bibr" rid="B19">19</xref>). This suggests that <italic>CYP4F2</italic> polymorphisms may be related to susceptibility to LC by affecting the metabolism of 20-HETE, although further verification is required. Studies have also indicated a significant association between <italic>CYP4F2</italic> polymorphisms and a variety of diseases, including ischemic stroke and various other cardiovascular and cerebrovascular diseases (<xref ref-type="bibr" rid="B12">12</xref>, <xref ref-type="bibr" rid="B20">20</xref>).</p>
<p>Our study focused on the association between <italic>CYP4F2</italic> polymorphisms and susceptibility to LC. Five sites were selected for statistical analyses: rs3093203, rs3093193, rs12459936, rs3093144, and rs3093110. However, none of these loci were found to be significantly associated with LC susceptibility under the allelic model or any of the five genetic models. The actual increase in LC risk may be underestimated due to the limited sample size. To further examine the potential influence of LC, we conducted a stratified analysis. Tobacco has long been recognized as an independent risk factor for tumorigenesis, as it contains many carcinogens, such as nitrosamines, polycyclic aromatic hydrocarbons, and volatile organic compounds (<xref ref-type="bibr" rid="B21">21</xref>). However, our analysis stratified by smoking revealed that the rs12459936 and rs3093110 loci were significantly associated with increased susceptibility to LC in the non-smoking population but not in the smoking population.</p>
<p>In addition, gender has been found to have a notable impact on the toxicity of therapeutic treatments and the response to them in many types of cancer. The underlying cause of this difference is likely related to a complex interplay of several factors, including sex hormones, which have been shown to affect the self-renewal of tumor stem cells, the tumor microenvironment, the immune system, and metabolism (<xref ref-type="bibr" rid="B22">22</xref>). It is well established that there are considerable differences in the immune system between men and women. In general, women have a stronger immune system than men, leading to distinct sex-based differences in both innate and adaptive immune responses. These disparities in immune systems likely play a role in cancer susceptibility between males and females (<xref ref-type="bibr" rid="B23">23</xref>). In our study, analysis stratified by gender was performed, and we found that rs309319, rs12459936, and rs3093110 all had a protective role against LC in females.</p>
<p>Taken together, our study observed that variants in <italic>CYP4F2</italic> were associated with LC susceptibility. However, our research had some limitations. First, the potential functional implications of <italic>CYP4F2</italic> polymorphisms were not addressed in this study. The expression data for <italic>CYP4F2</italic> in LC cases were sourced from the database. To properly elucidate the genetic mechanism of <italic>CYP4F2</italic> in LC, expression analysis of <italic>CYP4F2</italic> mRNA and annotation of the functional significance of variants are necessary. Second, the sample size was relatively small. In the following steps, we will perfect this information and expand the sample size to explore the molecular mechanism of <italic>CYP4F2</italic> polymorphisms affecting the development of LC.</p>
</sec>
<sec id="s5" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>, further inquiries can be directed to the corresponding author/s.</p>
</sec>
<sec id="s6" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Our study complied with the Declaration of Helsinki, and the protocol in our experience was approved by the Second Affiliated Hospital of Xi&#x2019;an Jiaotong University. All participants have been informed and provided written informed consent for the study.</p>
</sec>
<sec id="s7" sec-type="author-contributions">
<title>Author contributions</title>
<p>HS designed this study and drafted the manuscript. YZ performed the DNA extraction and genotyping. YW revised the manuscript and performed the data analysis. PF and YL performed the samples collection and information recording. HS conceived and supervised the study. 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 work was supported by the Natural Science Basic Research Plan in Shaanxi Province of China (2020JM-410) and the hospital fund of the Second Affiliated Hospital of Xi&#x2019;an Jiaotong University (RC(GG)201809).</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>We thank all volunteers in the study. We would also like to thank the clinicians and hospital staff who contributed to the collection of samples and data for our study.</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>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fonc.2023.1114218/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fonc.2023.1114218/full#supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
</sec>
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