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<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="publisher-id">1269291</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2023.1269291</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study</article-title>
<alt-title alt-title-type="left-running-head">Li et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fgene.2023.1269291">10.3389/fgene.2023.1269291</ext-link>
</alt-title>
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<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Honglin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
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<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhang</surname>
<given-names>Lei</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>&#x2020;</sup>
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<contrib contrib-type="author">
<name>
<surname>Yang</surname>
<given-names>Feiran</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author">
<name>
<surname>Feng</surname>
<given-names>Xiaoteng</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Fu</surname>
<given-names>Rong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<contrib contrib-type="author">
<name>
<surname>Zhao</surname>
<given-names>Ruohan</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
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<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Xiurong</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Li</surname>
<given-names>Huijie</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>First Clinical College</institution>, <institution>Shandong University of Traditional Chinese Medicine</institution>, <addr-line>Jinan</addr-line>, <addr-line>Shandong</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Oncology</institution>, <institution>Affiliated Hospital of Shandong University of Traditional Chinese Medicine</institution>, <addr-line>Jinan</addr-line>, <addr-line>Shandong</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<sup>3</sup>
<institution>Department of Cardiology</institution>, <institution>Longhua Hospital</institution>, <institution>Shanghai University of Traditional Chinese Medicine</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<sup>4</sup>
<institution>Department of Oncology</institution>, <institution>Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine</institution>, <addr-line>Shanghai</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/44701/overview">Jared C. Roach</ext-link>, Institute for Systems Biology (ISB), United States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2399600/overview">Aleksandra Czumaj</ext-link>, Medical University of Gdansk, Poland</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1510040/overview">Yeun-po Chiang</ext-link>, Emory University, United States</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Huijie Li, <email>2008lihuijie@163.com</email>
</corresp>
<fn fn-type="equal" id="fn001">
<label>
<sup>&#x2020;</sup>
</label>
<p>These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>16</day>
<month>11</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="collection">
<year>2023</year>
</pub-date>
<volume>14</volume>
<elocation-id>1269291</elocation-id>
<history>
<date date-type="received">
<day>01</day>
<month>08</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>02</day>
<month>11</month>
<year>2023</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2023 Li, Zhang, Yang, Feng, Fu, Zhao, Li and Li.</copyright-statement>
<copyright-year>2023</copyright-year>
<copyright-holder>Li, Zhang, Yang, Feng, Fu, Zhao, Li and Li</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> The causal relationship between lipid-lowering drug (LLD) use and lung cancer risk is controversial, and the role of sphingolipid metabolism in this effect remains unclear.</p>
<p>
<bold>Methods:</bold> Genome-wide association study data on low-density lipoprotein (LDL), apolipoprotein B (ApoB), and triglycerides (TG) were used to develop genetic instrumental variables (IVs) for LLDs. Two-step Mendelian randomization analyses were performed to examine the causal relationship between LLDs and lung cancer risk. The effects of ceramide, sphingosine-1-phosphate (S1P), and ceramidases on lung cancer risk were explored, and the proportions of the effects of LLDs on lung cancer risk mediated by sphingolipid metabolism were calculated.</p>
<p>
<bold>Results:</bold> <italic>APOB</italic> inhibition decreased the lung cancer risk in ever-smokers via ApoB (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.70&#x2013;0.92, <italic>p</italic> &#x3d; 0.010), LDL (OR 0.82, 95% CI 0.71&#x2013;0.96, <italic>p</italic> &#x3d; 0.040), and TG (OR 0.63, 95% CI 0.46&#x2013;0.83, <italic>p</italic> &#x3d; 0.015) reduction by 1 standard deviation (SD), decreased small-cell lung cancer (SCLC) risk via LDL reduction by 1 SD (OR 0.71, 95% CI 0.56&#x2013;0.90, <italic>p</italic> &#x3d; 0.016), and decreased the plasma ceramide level and increased the neutral ceramidase level. <italic>APOC3</italic> inhibition decreased the lung adenocarcinoma (LUAD) risk (OR 0.60, 95% CI 0.43&#x2013;0.84, <italic>p</italic> &#x3d; 0.039) but increased SCLC risk (OR 2.18, 95% CI 1.17&#x2013;4.09, <italic>p</italic> &#x3d; 0.029) via ApoB reduction by 1 SD. <italic>HMGCR</italic> inhibition increased SCLC risk via ApoB reduction by 1 SD (OR 3.04, 95% CI 1.38&#x2013;6.70, <italic>p</italic> &#x3d; 0.014). The <italic>LPL</italic> agonist decreased SCLC risk via ApoB (OR 0.20, 95% CI 0.07&#x2013;0.58, <italic>p</italic> &#x3d; 0.012) and TG reduction (OR 0.58, 95% CI 0.43&#x2013;0.77, <italic>p</italic> &#x3d; 0.003) while increased the plasma S1P level. <italic>PCSK9</italic> inhibition decreased the ceramide level. Neutral ceramidase mediated 8.1% and 9.5% of the reduced lung cancer risk in ever-smokers via ApoB and TG reduction by <italic>APOB</italic> inhibition, respectively, and mediated 8.7% of the reduced LUAD risk via ApoB reduction by <italic>APOC3</italic> inhibition.</p>
<p>
<bold>Conclusion:</bold> We elucidated the intricate interplay between LLDs, sphingolipid metabolites, and lung cancer risk. Associations of <italic>APOB</italic>, <italic>APOC3</italic>, and <italic>HMGCR</italic> inhibition and <italic>LPL</italic> agonist with distinct lung cancer risks underscore the multifaceted nature of these relationships. The observed mediation effects highlight the considerable influence of neutral ceramidase on the lung cancer risk reduction achieved by <italic>APOB</italic> and <italic>APOC3</italic> inhibition.</p>
</abstract>
<kwd-group>
<kwd>lipid-lowering drug</kwd>
<kwd>ceramide</kwd>
<kwd>ceramidase</kwd>
<kwd>sphingosine-1-phosphate</kwd>
<kwd>sphingolipid metabolism</kwd>
<kwd>lung cancer</kwd>
<kwd>Mendelian randomization study</kwd>
</kwd-group>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Human and Medical Genomics</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>1 Introduction</title>
<p>The incidence and mortality rates of lung cancer have consistently increased in accordance with global patterns of malignant tumor (<xref ref-type="bibr" rid="B48">Sharma, 2022</xref>). Despite continuous advancements and refinements in lung cancer treatment, the pursuit of effective prevention methods to mitigate the risk of developing this disease remains paramount (<xref ref-type="bibr" rid="B26">Kordiak et al., 2022</xref>). Observational studies have suggested a potential association between statin use and a reduced risk of lung cancer (<xref ref-type="bibr" rid="B25">Khurana et al., 2007</xref>; <xref ref-type="bibr" rid="B64">Yang et al., 2015</xref>; <xref ref-type="bibr" rid="B27">Kwon et al., 2019</xref>). However, a case&#x2013;control study conducted within the UK population reported an increased risk of lung cancer associated with long-term statin exposure (<xref ref-type="bibr" rid="B55">Vinogradova et al., 2011</xref>). Furthermore, recent Mendelian randomization (MR) studies have challenged the notion of a causal relationship (<xref ref-type="bibr" rid="B5">Carter et al., 2020</xref>; <xref ref-type="bibr" rid="B37">Min et al., 2023</xref>). Investigations of lipidomic profiles revealed that the use of statins, PCSK9 inhibitors, and fenofibrate, instead of NPC1L1 inhibitors, results in a reduction in plasma ceramide and sphingomyelin levels (<xref ref-type="bibr" rid="B39">Ng et al., 2014</xref>; <xref ref-type="bibr" rid="B52">Tarasov et al., 2014</xref>; <xref ref-type="bibr" rid="B38">Ng et al., 2015</xref>; <xref ref-type="bibr" rid="B11">Croyal et al., 2018</xref>). Nonetheless, findings regarding the impact of statins on plasma sphingosine-1-phosphate (S1P) levels have exhibited inconsistency (<xref ref-type="bibr" rid="B13">Egom et al., 2013</xref>; <xref ref-type="bibr" rid="B53">Therond and Chapman, 2022</xref>). Preclinical investigations have elucidated the significant role of sphingolipid metabolism in the pathogenesis of lung cancer (<xref ref-type="bibr" rid="B40">Ogretmen, 2018</xref>; <xref ref-type="bibr" rid="B36">Meng et al., 2021</xref>; <xref ref-type="bibr" rid="B51">Tang et al., 2023</xref>), although the precise relationship remains unclear (<xref ref-type="bibr" rid="B31">Lin et al., 2022</xref>). Among the sphingolipids, ceramide and S1P have garnered particular attention within lung cancer studies (<xref ref-type="bibr" rid="B18">Goldkorn et al., 2013</xref>). Notably, ceramidases play a pivotal role in the conversion of ceramide to S1P (<xref ref-type="bibr" rid="B44">Pyne and Pyne, 2010</xref>). While a case&#x2013;control study has uncovered associations between higher concentrations of plasma sphingosine-1-phosphate and ceramides and the increasing risk of lung cancer, these findings contradict the preclinical results that suggest that ceramides promote apoptosis in lung cancer cells (<xref ref-type="bibr" rid="B41">Ogretmen and Hannun, 2004</xref>; <xref ref-type="bibr" rid="B4">Carpinteiro et al., 2008</xref>; <xref ref-type="bibr" rid="B2">Alberg et al., 2013</xref>). Evidence on how lipid-lowering drugs (LLDs) may influence the risk of lung cancer by modulating sphingolipid metabolites remains elusive, along with the potential underlying mechanisms.</p>
<p>To explore the intricate relationships among LLDs, sphingolipid metabolites (including plasma ceramide, ceramidase, and S1P), and the risk of lung cancer, we employed a drug-target MR analysis (<xref ref-type="bibr" rid="B21">Holmes et al., 2021</xref>) to address a critical knowledge gap and provide valuable insights into potential chemoprevention in lung cancer.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>2 Materials and methods</title>
<sec id="s2-1">
<title>2.1 Study design</title>
<p>The results obtained from MR analysis, using data from genome-wide association studies (GWAS) to analyze the effects of exposure factors, closely resemble the findings of randomized controlled trials. This resemblance can be attributed to the random allocation of genetic variants during meiosis (<xref ref-type="bibr" rid="B65">Yarmolinsky et al., 2022</xref>). The expression and functionality of drug targets can be profoundly affected by genetic variations. Moreover, the impact of drugs can be anticipated through the genetic variability present in the genes encoding their protein targets (<xref ref-type="bibr" rid="B7">Chauquet et al., 2021</xref>). Similar to the previous study, instrumental variables (IVs) for LLDs were extracted from GWAS summary statistics related to low-density lipoprotein (LDL), apolipoprotein B (ApoB), and triglycerides (TG), enabling an analysis of the causal relationship between LLD exposure and the risk of developing lung cancer (<xref ref-type="bibr" rid="B59">Williams et al., 2020</xref>; <xref ref-type="bibr" rid="B60">Xiao et al., 2023</xref>). To clarify the mediating role of sphingolipid metabolites, we employed a two-step MR analysis. This involves utilizing the outcome variable from the initial MR analysis as the exposure variable for the subsequent MR analysis (<xref ref-type="bibr" rid="B1">Adams and Boutwell, 2021</xref>; <xref ref-type="bibr" rid="B62">Xu et al., 2022</xref>). Our analysis investigates the impact of LLDs on plasma sphingolipid metabolite (ceramide, S1P, and ceramidase) levels, alongside establishing a causal link between these sphingolipid metabolites and lung cancer risk. Additionally, we quantify the mediating influence of sphingolipid metabolites on the association between LLDs and lung cancer risk.</p>
</sec>
<sec id="s2-2">
<title>2.2 Genetic variant selection</title>
<p>Information about chromosomal and gene loci for the eight LLD targets (<xref ref-type="bibr" rid="B59">Williams et al., 2020</xref>), namely, <italic>ANGPTL3</italic>, <italic>APOB</italic>, <italic>APOC3</italic>, <italic>HMGCR</italic>, <italic>LPL</italic>, <italic>NPC1L1</italic>, <italic>PCSK9,</italic> and <italic>PPARA</italic>, was obtained from the National Center for Biotechnology Information gene database (<ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/gene">https://www.ncbi.nlm.nih.gov/gene</ext-link>) (see <xref ref-type="table" rid="T1">Table 1</xref> and <xref ref-type="sec" rid="s12">Supplementary Table S1</xref> for details).</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Information on lipid-lowering drug targets included in the studies.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Examples of drugs/class</th>
<th align="left">Target</th>
<th align="left">Gene encoding target</th>
<th align="left">HGNC ID</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Evinacumab</td>
<td align="left">Angiopoietin&#x2010;like protein 3 (ANGPTL3)</td>
<td align="left">
<italic>ANGPTL3</italic>
</td>
<td align="left">491</td>
</tr>
<tr>
<td align="left">Mipomersen</td>
<td align="left">Apo&#x2010;B 100 messenger RNA (ApoB&#x2010;100)</td>
<td align="left">
<italic>APOB</italic>
</td>
<td align="left">603</td>
</tr>
<tr>
<td align="left">Volanesorsen</td>
<td align="left">Apolipoprotein C&#x2010;3 (ApoC3)</td>
<td align="left">
<italic>APOC3</italic>
</td>
<td align="left">610</td>
</tr>
<tr>
<td align="left">Statins</td>
<td align="left">HMG&#x2010;CoA reductase (HMGCR)</td>
<td align="left">
<italic>HMGCR</italic>
</td>
<td align="left">5006</td>
</tr>
<tr>
<td align="left">Alipogene tiparvovec</td>
<td align="left">Lipoprotein lipase (LPL)</td>
<td align="left">
<italic>LPL</italic>
</td>
<td align="left">6677</td>
</tr>
<tr>
<td align="left">Ezetimibe</td>
<td align="left">Niemann&#x2013;Pick C1&#x2010;like protein 1 (NPC1L1)</td>
<td align="left">
<italic>NPC1L1</italic>
</td>
<td align="left">7897</td>
</tr>
<tr>
<td align="left">Evolocumab</td>
<td align="left">Proprotein convertase subtilisin/kexin type 9 (PCSK9)</td>
<td align="left">
<italic>PCSK9</italic>
</td>
<td align="left">20001</td>
</tr>
<tr>
<td align="left">Fibrates</td>
<td align="left">Peroxisome proliferator&#x2010;activated receptor alpha (PPAR&#x3b1;)</td>
<td align="left">
<italic>PPARA</italic>
</td>
<td align="left">9232</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>HGNC, HUGO Gene Nomenclature Committee.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>We accessed the Medical Research Council Integrative Epidemiology Unit (IEU) OpenGWAS database (<ext-link ext-link-type="uri" xlink:href="https://gwas.mrcieu.ac.uk/">https://gwas.mrcieu.ac.uk/</ext-link>) and obtained GWAS summary statistics for LDL (ieu-a-300, <italic>n</italic> &#x3d; 173,082), TG (ieu-a-302, <italic>n</italic> &#x3d; 177,861), and ApoB (ieu-b-108, <italic>n</italic> &#x3d; 439,214). These lipid traits were selected as they represent downstream substances influenced by the effectiveness of LLDs (<xref ref-type="bibr" rid="B59">Williams et al., 2020</xref>).</p>
<p>To generate IVs for LLDs, we used the LLD target genes, involving four targets of drugs for decreasing LDL (<italic>APOB</italic>, <italic>HMGCR</italic>, <italic>NPC1L1</italic>, and <italic>PCSK9</italic>), six targets of drugs for decreasing ApoB (<italic>ANGPTL3</italic>, <italic>APOB</italic>, <italic>APOC3</italic>, <italic>LPL</italic>, <italic>PPARA</italic>, and <italic>PCSK9</italic>), and five targets of drugs for decreasing TG (<italic>ANGPTL3</italic>, <italic>APOB</italic>, <italic>APOC3</italic>, <italic>PPARA</italic>, and <italic>LPL</italic>) (<xref ref-type="bibr" rid="B60">Xiao et al., 2023</xref>). We confined the single-nucleotide polymorphisms (SNPs) to those located within a 100-kb range surrounding the LLD target genes that exhibited genome-wide significance in their association with the lipid traits (<italic>p</italic> &#x3c; 5.0 &#xd7; 10<sup>&#x2212;8</sup>). To maximize the IV strength for each LLD target, SNPs were allowed to be in weak linkage disequilibrium (<italic>r</italic>
<sup>2</sup> &#x3c; 0.30, window size &#x3d; 10,000&#xa0;kb) with each other (<xref ref-type="bibr" rid="B61">Xie et al., 2023</xref>). Given the absence of any genetic variation in <italic>PPARA</italic> during the selection process, it has been excluded from subsequent evaluations (<xref ref-type="bibr" rid="B30">Li et al., 2023</xref>).</p>
<p>IV strength was assessed using the F-statistic, with values &#x3e; 10 indicating non-weak IVs (<xref ref-type="bibr" rid="B12">Duan et al., 2021</xref>). Additionally, for a positive control analysis, GWAS summary statistics for coronary heart disease (CHD) obtained from CARDIoGRAM (ieu-a-8, <italic>n</italic> &#x3d; 86,995) were utilized as the outcome measure (<xref ref-type="bibr" rid="B22">Huang et al., 2021</xref>).</p>
</sec>
<sec id="s2-3">
<title>2.3 Sources of GWAS summary statistics for ceramidases and lung cancer</title>
<p>GWAS summary statistics for plasma ceramide (GCST90025189, <italic>n</italic> &#x3d; 6,057) and S1P levels (GCST90199657, <italic>n</italic> &#x3d; 8,246) were obtained from GWAS Catalog (<xref ref-type="bibr" rid="B3">Cadby et al., 2022</xref>; <xref ref-type="bibr" rid="B9">Chen et al., 2023</xref>). Ceramidases are classified into acid, neutral, and alkaline ceramidases based on their pH characteristics. However, in the available datasets, there were only GWAS summary statistics for acid and neutral ceramidases (prot-a-178 and prot-a-179, <italic>n</italic> &#x3d; 3,301) from the IEU OpenGWAS database (<xref ref-type="bibr" rid="B50">Sun et al., 2018</xref>). The GWAS summary statistics for lung cancer were sourced from the publicly available dataset published by <xref ref-type="bibr" rid="B35">McKay et al. (2017)</xref>, which included lung cancer cases (<italic>n</italic> &#x3d; 85,716) as well as subgroups of ever-smokers (<italic>n</italic> &#x3d; 40,187), never-smokers (<italic>n</italic> &#x3d; 9,859), lung squamous cell carcinoma (LUSC) (<italic>n</italic> &#x3d; 63,053), lung adenocarcinoma (LUAD) (<italic>n</italic> &#x3d; 66,756), and small-cell lung cancer (SCLC) (<italic>n</italic> &#x3d; 24,108). <xref ref-type="table" rid="T2">Table 2</xref> lists the demographic characteristics of detailed lung cancer GWAS summary statistics.</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Demographic characteristics of GWAS statistics for lung cancer.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Subgroup</th>
<th align="left">No. of patients</th>
<th align="left">No. of controls</th>
<th align="left">No. of sum</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Overall lung cancer</td>
<td align="left">29,266</td>
<td align="left">56,450</td>
<td align="left">85,716</td>
</tr>
<tr>
<td colspan="4" align="left">Age</td>
</tr>
<tr>
<td align="left">&#x2003;&#x3c;&#x3d;50</td>
<td align="left">3,112</td>
<td align="left">6,032</td>
<td align="left">9,144</td>
</tr>
<tr>
<td align="left">&#x2003;&#x3e;50</td>
<td align="left">23,025</td>
<td align="left">44,075</td>
<td align="left">67,100</td>
</tr>
<tr>
<td colspan="4" align="left">Sex</td>
</tr>
<tr>
<td align="left">&#x2003;Male</td>
<td align="left">18,208</td>
<td align="left">27,178</td>
<td align="left">45,386</td>
</tr>
<tr>
<td align="left">&#x2003;Female</td>
<td align="left">11,059</td>
<td align="left">24,069</td>
<td align="left">35,128</td>
</tr>
<tr>
<td colspan="4" align="left">Smoking status</td>
</tr>
<tr>
<td align="left">&#x2003;Never</td>
<td align="left">2,355</td>
<td align="left">7,504</td>
<td align="left">9,859</td>
</tr>
<tr>
<td align="left">&#x2003;Ever</td>
<td align="left">23,223</td>
<td align="left">16,964</td>
<td align="left">40,187</td>
</tr>
<tr>
<td align="left">&#x2003;Former</td>
<td align="left">9,037</td>
<td align="left">8,554</td>
<td align="left">17,591</td>
</tr>
<tr>
<td align="left">&#x2003;Current</td>
<td align="left">13,356</td>
<td align="left">7,477</td>
<td align="left">20,833</td>
</tr>
<tr>
<td colspan="4" align="left">Histology</td>
</tr>
<tr>
<td align="left">&#x2003;LUAD</td>
<td align="left">11,273</td>
<td align="left">55,483</td>
<td align="left">66,756</td>
</tr>
<tr>
<td align="left">&#x2003;LUSC</td>
<td align="left">7,426</td>
<td align="left">55,627</td>
<td align="left">63,053</td>
</tr>
<tr>
<td align="left">&#x2003;SCLC</td>
<td align="left">2,664</td>
<td align="left">21,444</td>
<td align="left">24,108</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SCLC, small-cell lung cancer.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s2-4">
<title>2.4 Statistical analysis</title>
<p>MR analysis for the effects of LLDs on the risk of lung cancer and sphingolipid metabolite levels was calculated. The clumping procedure (<italic>r</italic>
<sup>2</sup> &#x3c; 0.001, window size &#x3d; 10,000&#xa0;kb) was performed to remove linkage disequilibrium, and SNPs that exhibited genome-wide significance (<italic>p</italic> &#x3c; 5.0 &#xd7; 10<sup>&#x2212;8</sup>) were selected as IVs for ceramide, S1P, and acid/neutral ceramidases (<xref ref-type="bibr" rid="B14">Fang et al., 2023</xref>) (<xref ref-type="sec" rid="s12">Supplementary Table S3</xref>).</p>
<p>When only one SNP was available as the IV, we utilized the Wald ratio method. Otherwise, we employed five methods: inverse variance-weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode. A reliable causal relationship was defined as follows: estimates from the IVW method or the Wald ratio method were statistically significant (<italic>p</italic> &#x3c; 0.05) and were consistent with the direction of causal estimates computed by the MR-Egger method (Wang et al.).</p>
<p>To explore the mediating effect of sphingolipid metabolites regarding the effect of LLDs on lung cancer risk, a two-step MR analysis was performed. The proportion mediated by sphingolipid metabolites was calculated using the following formula (<xref ref-type="bibr" rid="B8">Chen et al., 2022</xref>; <xref ref-type="bibr" rid="B67">Zhao et al., 2022</xref>): <inline-formula id="inf1">
<mml:math id="m1">
<mml:mrow>
<mml:mi mathvariant="normal">&#x395;</mml:mi>
<mml:mrow>
<mml:mfenced open="(" close=")" separators="|">
<mml:mrow>
<mml:mo>%</mml:mo>
</mml:mrow>
</mml:mfenced>
</mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">&#x3a3;</mml:mi>
<mml:mrow>
<mml:mi>k</mml:mi>
<mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:msubsup>
<mml:mi>&#x3b2;</mml:mi>
<mml:mrow>
<mml:mn>1</mml:mn>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
<mml:mn>2</mml:mn>
<mml:mi>k</mml:mi>
</mml:mrow>
<mml:mrow>
<mml:msubsup>
<mml:mi mathvariant="normal">&#x3a3;</mml:mi>
<mml:mrow>
<mml:mi>k</mml:mi>
<mml:mrow>
<mml:mo>&#x3d;</mml:mo>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:msubsup>
<mml:mi>&#x3b2;</mml:mi>
<mml:mrow>
<mml:mn>3</mml:mn>
<mml:mo>&#x2b;</mml:mo>
</mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
<mml:mrow>
<mml:mn>1</mml:mn>
<mml:mo>&#xd7;</mml:mo>
</mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
<mml:mn>2</mml:mn>
<mml:mi>k</mml:mi>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</inline-formula>, where &#x3b2;1 represents the estimated effect of LLDs on sphingolipid metabolites, &#x3b2;2 represents the estimated effect of sphingolipid metabolites on lung cancer, and &#x3b2;3 represents the estimated direct effect of LLDs on lung cancer. The product of &#x3b2;1 and &#x3b2;2 is considered the indirect effect, while (&#x3b2;3 &#x2b; &#x3b2;1 &#xd7; &#x3b2;2) represents the total effect. The proportion is calculated when &#x3b2;1, &#x3b2;2, and &#x3b2;3 exhibit statistical significance, as this quantifies the proportion of mediation by sphingolipid metabolites within the total effect (<xref ref-type="bibr" rid="B8">Chen et al., 2022</xref>). To account for multiple statistical tests and reduce false-positive results, we controlled the false discovery rate (FDR) using the Benjamini&#x2013;Hochberg method. Adjusted <italic>p</italic> &#x3c; 0.05 was considered statistically significant (<xref ref-type="bibr" rid="B34">Liu et al., 2023</xref>).</p>
<p>To assess the sensitivity of the MR results, we conducted MR-Egger intercept tests and calculated Cochran&#x2019;s Q statistic to evaluate pleiotropy and heterogeneity. A significance level of <italic>p</italic> &#x3c; 0.05 was applied to determine statistical significance. All MR analyses were performed using the &#x201c;<italic>TwoSampleMR</italic>&#x201d; package (version 0.5.6) in R software (version 4.1.1), and forest plots were generated using the &#x201c;<italic>forestploter</italic>&#x201d; package.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>3 Results</title>
<sec id="s3-1">
<title>3.1 Positive control analysis</title>
<p>We investigated the causal associations between the IVs for inhibiting the LLD targets and CHD. Notably, the effect of <italic>ANGPTL3</italic> inhibition, whether via ApoB or TG reduction, did not show statistically significant results in the positive control analysis. Consequently, <italic>ANGPTL3</italic> inhibition was excluded from the subsequent MR analysis. The inhibition of the remaining six LLD targets exhibited a significant causal association, with a reduced risk of CHD. <xref ref-type="fig" rid="F1">Figure 1</xref> and <xref ref-type="sec" rid="s12">Supplementary Table S4</xref> show detailed results, illustrating the magnitude and significance of these associations.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Results of positive control analysis. LDL, low-density lipoprotein; ApoB, apolipoprotein B; TG, triglycerides; SNPs, single-nucleotide polymorphisms; FDR, false discovery rate.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g001.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>3.2 The effects of LLDs on lung cancer risk</title>
<p>
<xref ref-type="fig" rid="F2">Figures 2</xref>, <xref ref-type="fig" rid="F3">3</xref> and <xref ref-type="sec" rid="s12">Supplementary Tables S5&#x2013;S10</xref> show the results of our MR analysis, estimating the effects of LLDs on the risk of lung cancer (overall) and in five subgroups (lung cancer in ever-smokers, lung cancer in never-smokers, LUSC, LUAD, and SCLC).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Heatmap of the causal relationship between LLDs, lung cancer, and ceramidases.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g002.tif"/>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Estimated effects of LLD on the risk of <bold>(A)</bold> lung cancer (overall); <bold>(B)</bold> lung cancer in ever-smokers; <bold>(C)</bold> lung cancer in never-smokers; <bold>(D)</bold> LUSC; <bold>(E)</bold> LUAD; and <bold>(F)</bold> SCLC. FDR-adjusted <italic>p</italic>-values with statistical significance (&#x3c;0.05) are shown in bold. LDL, low-density lipoprotein; ApoB, apolipoprotein B; TG, triglycerides; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SCLC, small-cell lung cancer; FDR, false discovery rate.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g003.tif"/>
</fig>
<p>
<italic>APOB</italic> inhibition significantly decreased the risk of lung cancer in ever-smokers [odds ratio (OR) 0.81, 95% confidence interval (CI) 0.70&#x2013;0.92, <italic>p</italic> &#x3d; 0.01] and SCLC (OR 0.70, 95% CI 0.55&#x2013;0.88, <italic>p</italic> &#x3d; 0.014) via ApoB reduction by 1 standard deviation (SD). Additionally, <italic>APOB</italic> inhibition decreased the risk of lung cancer in ever-smokers via LDL (OR 0.82, 95% CI 0.71&#x2013;0.96, <italic>p</italic> &#x3d; 0.040) and TG (OR 0.62, 95% CI 0.46&#x2013;0.83, <italic>p</italic> &#x3d; 0.015) reduction by 1 SD. Furthermore, <italic>APOB</italic> inhibition decreased the risk of SCLC via LDL reduction (OR 0.71, 95% CI 0.56&#x2013;0.90, <italic>p</italic> &#x3d; 0.016).</p>
<p>
<italic>APOC3</italic> inhibition decreased the risk of LUAD via ApoB reduction by 1 SD (OR 0.60, 95% CI 0.43&#x2013;0.84, <italic>p</italic> &#x3d; 0.039). However, it increased the risk of SCLC via ApoB reduction (OR 2.18, 95% CI 1.17&#x2013;4.09, <italic>p</italic> &#x3d; 0.029).</p>
<p>Interestingly, <italic>HMGCR</italic> inhibition increased the risk of SCLC via ApoB reduction by 1 SD (OR 3.04, 95% CI 1.38&#x2013;6.70, <italic>p</italic> &#x3d; 0.014). On the other hand, the <italic>LPL</italic> agonist decreased the risk of SCLC via ApoB (OR 0.20, 95% CI 0.07&#x2013;0.58, <italic>p</italic> &#x3d; 0.012) and TG (OR 0.58, 95% CI 0.43&#x2013;0.77, <italic>p</italic> &#x3d; 0.003) reduction by 1 SD. The MR analysis conducted exhibited neither pleiotropy nor heterogeneity.</p>
</sec>
<sec id="s3-3">
<title>3.3 The effects of LLDs on ceramide, S1P, and ceramidases</title>
<p>
<xref ref-type="fig" rid="F4">Figure 4</xref> and <xref ref-type="sec" rid="s12">Supplementary Tables S11&#x2013;S14</xref> show the results of MR analysis, estimating the effects of LLDs on plasma ceramide, S1P, and ceramidases levels. <italic>APOB</italic> inhibition decreased the plasma ceramide level via ApoB (OR 0.67, 95% CI 0.55&#x2013;0.82, <italic>p</italic> &#x3d; 5.21 &#xd7; 10<sup>&#x2212;4</sup>), LDL (OR 0.60, 95% CI 0.48&#x2013;0.74, <italic>p</italic> &#x3d; 3.76 &#xd7; 10<sup>&#x2212;5</sup>), and TG (OR 0.47, 95% CI 0.29&#x2013;0.77, <italic>p</italic> &#x3d; 0.007) reduction by 1 SD. Similarly, <italic>PCSK9</italic> inhibition decreased the plasma ceramide level via ApoB (OR 0.59, 95% CI 0.43&#x2013;0.80, <italic>p</italic> &#x3d; 0.003) and LDL (OR 0.76, 95% CI 0.60&#x2013;0.98, <italic>p</italic> &#x3d; 0.061) reduction by 1 SD. Conversely, the <italic>LPL</italic> agonist increased the plasma ceramide level via ApoB (OR 3.45, 95% CI 1.23&#x2013;9.66, <italic>p</italic> &#x3d; 0.044) reduction by 1 SD and increased the plasma S1P level via ApoB (OR 2.71, 95% CI 1.63&#x2013;4.51, <italic>p</italic> &#x3d; 7.03 &#xd7; 10<sup>&#x2212;4</sup>) and TG (OR 1.34, 95% CI 1.19&#x2013;1.52, <italic>p</italic> &#x3d; 2.88 &#xd7; 10<sup>&#x2212;5</sup>) reduction by 1 SD. Interestingly, <italic>APOB</italic> inhibition increased the plasma neutral ceramidase level via ApoB (OR 1.27, 95% CI 1.07&#x2013;1.51, <italic>p</italic> &#x3d; 0.03) and TG (OR 1.90, 95% CI 1.17&#x2013;3.09, <italic>p</italic> &#x3d; 0.034) reduction by 1 SD. Although <italic>APOB</italic> inhibition increased the plasma neutral ceramidase level via LDL reduction, it did not reach statistical significance after correction (OR 1.25, 95% CI 1.03&#x2013;1.51, <italic>p</italic> &#x3d; 0.072). However, LLDs did not significantly impact plasma acid ceramidase levels. The MR analysis exhibited neither pleiotropy nor heterogeneity.</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Estimated effects of LLD on plasma <bold>(A)</bold> ceramide; <bold>(B)</bold> S1P; <bold>(C)</bold> neutral ceramidase; and <bold>(D)</bold> acid ceramidase levels. FDR-adjusted <italic>p</italic>-values with statistical significance (&#x3c;0.05) are shown in bold. LDL, low-density lipoprotein; ApoB, apolipoprotein B; TG, triglycerides; FDR, false discovery rate; S1P, sphingosine-1-phosphate.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g004.tif"/>
</fig>
</sec>
<sec id="s3-4">
<title>3.4 The effects of sphingolipid metabolites on lung cancer risk</title>
<p>Our MR analysis revealed that an increase in the plasma neutral ceramidase level by 1 SD decreased the risk of overall lung cancer (OR 0.95, 95% CI 0.91&#x2013;0.99, <italic>p</italic> &#x3d; 0.032), lung cancer in ever-smokers (OR 0.92, 95% CI 0.88&#x2013;0.98, <italic>p</italic> &#x3d; 0.024), LUAD (OR 0.93, 95% CI 0.88&#x2013;0.99, <italic>p</italic> &#x3d; 0.043), and LUSC (OR 0.93, 95% CI 0.86&#x2013;0.99, <italic>p</italic> &#x3d; 0.041) (<xref ref-type="fig" rid="F5">Figure 5A</xref>; <xref ref-type="sec" rid="s12">Supplementary Table S15</xref>). However, there was no evidence of a causal relationship between the plasma acid ceramidase level and risk of lung cancer (<xref ref-type="fig" rid="F5">Figure 5B</xref>; <xref ref-type="sec" rid="s12">Supplementary Table S16</xref>). The plasma ceramide levels exhibited a positive correlation with heightened lung cancer risk in never-smokers; however, this relationship did not achieve statistical significance following correction (OR 1.49, 95% CI 1.10&#x2013;2.01, <italic>p</italic> &#x3d; 0.054) (<xref ref-type="fig" rid="F5">Figure 5C</xref>; <xref ref-type="sec" rid="s12">Supplementary Table S17</xref>). As the IVs of S1P did not intersect with the SNPs present in the GWAS data for lung cancer, the execution of this MR analysis was rendered unfeasible. The MR analysis exhibited neither pleiotropy nor heterogeneity.</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Estimated effects of plasma <bold>(A)</bold> neutral ceramidase; <bold>(B)</bold> acid ceramidase; and <bold>(C)</bold> ceramide levels on lung cancer risk. FDR-adjusted <italic>p</italic>-values with statistical significance (&#x3c;0.05) are shown in bold. LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; SCLC, small-cell lung cancer; FDR, false discovery rate.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g005.tif"/>
</fig>
</sec>
<sec id="s3-5">
<title>3.5 Analysis of the mediating effects of sphingolipid metabolites</title>
<p>The aforementioned results suggested that neutral ceramidase plays a mediating role in the effect of LLDs on lung cancer risk. <xref ref-type="table" rid="T3">Table 3</xref> shows the proportions mediated by neutral ceramidase regarding the negative effect of <italic>APOB</italic> inhibition on lung cancer risk in smokers via ApoB and TG reduction (8.1% and 9.5%, respectively) and the negative effect of <italic>APOC3</italic> inhibition on LUAD risk via ApoB reduction (8.7%). <xref ref-type="fig" rid="F6">Figure 6</xref> illustrates schematic diagrams of the direct and indirect effects of LLDs on lung cancer and the mediation by neutral ceramidase.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Proportions mediated by neutral ceramidase regarding the effects of LLD on lung cancer risk.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="center">LLD target</th>
<th align="center">Lung cancer subgroup</th>
<th align="center">
<inline-formula id="inf2">
<mml:math id="m2">
<mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>1</th>
<th align="center">
<inline-formula id="inf3">
<mml:math id="m3">
<mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
</mml:mrow>
</mml:math>
</inline-formula>2</th>
<th align="center">
<inline-formula id="inf4">
<mml:math id="m4">
<mml:mrow>
<mml:mi>&#x3b2;</mml:mi>
<mml:mn>3</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">Proportion (%)</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="center">
<italic>APOB</italic> (via ApoB reduction)</td>
<td align="center">Ever-smokers</td>
<td align="center">0.241</td>
<td align="center">&#x2212;0.079</td>
<td align="center">&#x2212;0.216</td>
<td align="center">8.1</td>
</tr>
<tr>
<td align="center">
<italic>APOB</italic> (via TG reduction)</td>
<td align="center">Ever-smokers</td>
<td align="center">0.642</td>
<td align="center">&#x2212;0.079</td>
<td align="center">&#x2212;0.486</td>
<td align="center">9.5</td>
</tr>
<tr>
<td align="center">
<italic>APOC3</italic> (via ApoB reduction)</td>
<td align="center">LUAD</td>
<td align="center">0.716</td>
<td align="center">&#x2212;0.069</td>
<td align="center">&#x2212;0.513</td>
<td align="center">8.7</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>LLD, lipid-lowering drugs; ApoB, apolipoprotein B; TG, triglycerides; LUAD, lung adenocarcinoma.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Overview of the direct and indirect effects of LLD on lung cancer risk, and the effects of neutral ceramidase. The effects of LLD on lung cancer risk involving mediating effects of neutral ceramidase are highlighted in red. LDL, low-density lipoprotein; ApoB, apolipoprotein B; TG, triglycerides.</p>
</caption>
<graphic xlink:href="fgene-14-1269291-g006.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>4 Discussion</title>
<p>In this drug-target MR analysis, we assessed the effects of LLDs on lung cancer risk via six LLD targets (<italic>APOB</italic>, <italic>APOC3</italic>, <italic>HMGCR</italic>, <italic>LPL</italic>, <italic>NPC1L1</italic>, and <italic>PCSK9</italic>), comprising 12 lipid-lowering pathways. Our findings revealed that <italic>APOB</italic> inhibition decreased the lung cancer risk in ever-smokers and SCLC. <italic>APOC3</italic> inhibition decreased the LUAD risk but increased the SCLC risk. Additionally, <italic>HMGCR</italic> inhibition increased the risk of SCLC, whereas the <italic>LPL</italic> agonist decreased the risk of SCLC.</p>
<p>We delved deeper into the mediating mechanisms involving sphingolipid metabolites within these processes. Our investigation revealed that inhibiting <italic>APOB</italic>, <italic>APOC3</italic>, and <italic>HMGCR</italic> increased plasma neutral ceramidase levels. Notably, we observed that neutral ceramidase played a protective role in various forms of lung cancer, including overall lung cancer, lung cancer in ever-smokers, LUAD, and LUSC. These findings highlight the potential therapeutic significance of neutral ceramidase as a mitigating factor in the development and progression of various types of lung cancer. In addition, we found that <italic>APOB</italic> and <italic>PCSK9</italic> inhibition led to a reduction in plasma ceramide levels, while the <italic>LPL</italic> agonist increased plasma ceramide and S1P levels. Nonetheless, no substantiated evidence has emerged to establish a causal link between ceramides and the risk of lung cancer.</p>
<p>Several MR studies have explored the potential preventive effects of LLDs on malignancies (<xref ref-type="bibr" rid="B5">Carter et al., 2020</xref>; <xref ref-type="bibr" rid="B33">Liu et al., 2021a</xref>; <xref ref-type="bibr" rid="B37">Min et al., 2023</xref>); however, these investigations have primarily focused on statins and have not included stratified analysis of different subtypes of lung cancer. In contrast to previous MR studies, our study expands the range of LLD targets under consideration. In our positive control analysis, we observed that <italic>ANGPTL3</italic> inhibition did not achieve statistical significance. This finding aligns with the results reported by <xref ref-type="bibr" rid="B56">Wang et al. (2021)</xref>. We made the decision not to conduct further analysis for <italic>ANGPTL3</italic> inhibition in subsequent MR analysis.</p>
<p>In contrast to previous MR analysis of the effects of statins on lung cancer risk, our study reveals novel findings that <italic>HMGCR</italic> inhibition increases the risk of SCLC, as does <italic>APOC3</italic> inhibition. This aligns with the findings reported by Vinogradova et al., where the link between statins and heightened lung cancer risk persisted even after adjusting for cardiovascular factors. However, their investigation lacked a subgroup analysis focusing on lung cancer types (<xref ref-type="bibr" rid="B55">Vinogradova et al., 2011</xref>). Moreover, evidence hints at the potential for long-term statin use to heighten cancer risk in women (<xref ref-type="bibr" rid="B16">Friedman et al., 2008</xref>). The mechanistic underpinnings behind the potential of statins in increasing SCLC risk remain elusive. We posited that this effect might stem from off-target consequences associated with prolonged statin use (<xref ref-type="bibr" rid="B32">Liu et al., 2021b</xref>; <xref ref-type="bibr" rid="B23">Jiang et al., 2023</xref>).</p>
<p>Immunohistochemical assessments of lung cancer tissue samples have unveiled lower protein expression of <italic>APOC3</italic> in SCLC compared to normal lung tissue. This discrepancy might propose a pathway through which <italic>APOC3</italic> inhibitors heighten the vulnerability to SCLC (<xref ref-type="bibr" rid="B49">Shi et al., 2016</xref>). Furthermore, an investigation under the UK&#x2019;s Early Access to Medicines Scheme reported instances of lung cancer metastasis after the prolonged utilization of <italic>APOC3</italic> inhibitors (volanesorsen) (<xref ref-type="bibr" rid="B24">Jones et al., 2023</xref>). Our findings strongly advocate for heightened vigilance concerning the potential for <italic>APOC3</italic> inhibitors to increase lung cancer risk.</p>
<p>While alipogene tiparvovec acts as an <italic>LPL</italic> agonist, its limited market presence and high cost (<xref ref-type="bibr" rid="B47">Senior, 2017</xref>) have left the landscape of observational inquiries into its long-term cancer risk ambiguous. Nonetheless, our study propounds a shielding effect of <italic>LPL</italic> agonists against SCLC susceptibility. Moreover, <xref ref-type="bibr" rid="B6">Cerne et al. (2007)</xref> reported diminished <italic>LPL</italic> gene expression within lung cancer tissues as opposed to normal tissues, potentially reinforcing the notion of <italic>LPL</italic> agonists mitigating lung cancer risk.</p>
<p>The observed causal relationships between <italic>APOB</italic> inhibition and reduced lung cancer risk in ever-smokers and LUAD risk are particularly noteworthy, as neutral ceramidase plays a significant mediating role in these relationships. In recent years, there has been increasing research into the role and mechanisms of neutral ceramidase in malignancies (<xref ref-type="bibr" rid="B10">Coant and Hannun, 2019</xref>). Preclinical studies have demonstrated that inhibiting neutral ceramidase can prevent the occurrence and progression of colon cancer (<xref ref-type="bibr" rid="B17">Garcia-Barros et al., 2016</xref>), whereas inhibiting acid ceramidase can inhibit the proliferation of non-small-cell lung cancer and enhance its sensitivity to cisplatin (<xref ref-type="bibr" rid="B66">Yildiz-Ozer et al., 2018</xref>; <xref ref-type="bibr" rid="B58">White-Gilbertson et al., 2019</xref>). However, observational studies have presented contrasting findings, with high levels of acid ceramidase being associated with improved prognosis in breast and ovarian cancers (<xref ref-type="bibr" rid="B45">Ruckhaberle et al., 2009</xref>; <xref ref-type="bibr" rid="B20">Hanker et al., 2013</xref>; <xref ref-type="bibr" rid="B46">Sanger et al., 2015</xref>). The conflicting results from clinical observational studies and preclinical studies suggest that ceramidases may have pleiotropic effects on the occurrence, treatment, and prognosis of cancers. These findings highlight the complex and multifaceted nature of ceramidases in cancer-related processes. In our MR analysis, we identified a causal relationship between increased neutral ceramidase and a reduced risk of lung cancer in ever-smokers. This suggests that neutral ceramidase may play a critical role in the development of smoking-induced lung cancer, with <italic>APOB</italic> inhibition potentially acting as a protective factor. Further research is warranted to elucidate the precise mechanisms underlying their effects and to reconcile the disparities observed among different types of cancer.</p>
<p>Ceramidases play essential regulatory roles in the onset and advancement of various cancer forms (<xref ref-type="bibr" rid="B46">Sanger et al., 2015</xref>; <xref ref-type="bibr" rid="B17">Garcia-Barros et al., 2016</xref>) and are pivotal in sphingolipid metabolism for converting ceramides into sphingosine, S1P, and fatty acids (<xref ref-type="bibr" rid="B42">Parveen et al., 2019</xref>). The sphingolipid metabolism pathway proves indispensable in the metastatic process of lung cancer (<xref ref-type="bibr" rid="B43">Pyne et al., 2018</xref>; <xref ref-type="bibr" rid="B10">Coant and Hannun, 2019</xref>); its potential as a novel therapeutic target holds significant promise for augmenting the efficacy of tumor treatments (<xref ref-type="bibr" rid="B54">Vijayan et al., 2019</xref>), and animal experiments have demonstrated that <italic>NPC1L1</italic> inhibition can modulate sphingolipid metabolism (<xref ref-type="bibr" rid="B63">Yamanashi et al., 2020</xref>). Although observational studies have indicated an association between statin use and reduced plasma ceramide levels, there is limited information on the impact on ceramidase levels.</p>
<p>In alignment with the findings of a previous study, our study identifies increased plasma ceramide levels as a contributing factor to heightened lung cancer risk among non-smokers, albeit with nominal statistical significance (<xref ref-type="bibr" rid="B2">Alberg et al., 2013</xref>). Preclinical studies have shown that exposure to cigarette smoke leads to increased ceramide levels in the lung tissue of mice (<xref ref-type="bibr" rid="B15">Filosto et al., 2011</xref>; <xref ref-type="bibr" rid="B19">Goldkorn et al., 2014</xref>; <xref ref-type="bibr" rid="B29">Lavrynenko et al., 2020</xref>); the contribution of ceramides to the heightened risk of smoking-related lung cancer necessitates prospective investigations for conclusive evidence. Consistent with previous observational studies, we found that <italic>PCSK9</italic> inhibition reduced plasma ceramide levels (<xref ref-type="bibr" rid="B52">Tarasov et al., 2014</xref>), and <italic>APOB</italic> inhibition had the same effect. In addition, our results revealed that the inhibition of <italic>LPL</italic> could increase plasma S1P levels. However, owing to the non-convergence of the IVs for S1P in the lung cancer GWAS dataset, an analysis of the association between S1P and lung cancer risk was precluded. Notably, <xref ref-type="bibr" rid="B2">Alberg et al. (2013)</xref> observed an increased risk of lung cancer with increased S1P levels.</p>
<p>The strength of our study resides in its augmentation of LLD targets and lipid-lowering pathways. It amplifies the scrutiny of distinct lung cancer subgroups. More importantly, we delve into the mediating effect of sphingolipid metabolites, thus elucidating the intricate mechanism underpinning how LLD influences lung cancer risk. However, it is important to acknowledge the limitations of our study. First, MR analyses could not be performed on two of the target genes of LLD: <italic>ANGPTL3</italic> and <italic>PPARA</italic>. This limitation arises from the inability of IVs to establish positive control associations or the unavailability of suitable genetic variants. Furthermore, given the intricate pharmacological mechanisms underlying these drugs, our MR analysis was unable to assess their potential off-target effects. Second, IVs employed as proxies for lifetime exposure to LLD may be limited in their ability to capture the effects of short-term drug exposure. Additionally, our study is constrained to examining causal directions and does not enable a precise estimation of dosage effects or the cumulative impact of multiple medications. Third, although the F-statistic for the genetic variants exceeds 10, indicating a low probability of weak instrument bias, statistical power is constrained due to the limited availability of only two SNPs as IVs for acidic and neutral ceramidases (<xref ref-type="bibr" rid="B28">Larsson et al., 2019</xref>). Moreover, the relatively small sample size utilized for the assessment of sphingolipids raises concerns regarding potential selection bias. As a result, the interpretation of our findings should be exercised with caution. Fourth, despite sensitivity analyses not yielding statistically significant outcomes, it remains essential to acknowledge the potential impact of confounding variables and horizontal pleiotropy, given the possibility of SNPs residing in a state of weak linkage disequilibrium (<italic>r</italic>
<sup>2</sup> &#x3c; 0.30). Fifth, the absence of GWAS summary statistics for sphingolipids and lung cancer across diverse populations precludes validation. MR studies cannot substitute randomized controlled trials, and further clinical and pharmacoepidemiological studies are essential for triangulating. Sixth, the study population predominantly comprised individuals of European ancestry, which may limit the generalizability of our findings to other racial and ethnic groups.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>5 Conclusion</title>
<p>We elucidated the intricate interplay between LLDs, sphingolipid metabolites, and lung cancer risk. Associations of <italic>APOB</italic>, <italic>APOC3</italic>, and <italic>HMGCR</italic> inhibition and the <italic>LPL</italic> agonist with distinct lung cancer risks underscore the multifaceted nature of these relationships. The observed mediation effects highlight the considerable influence of neutral ceramidase on lung cancer risk reduction achieved by <italic>APOB</italic> and <italic>APOC3</italic> inhibition.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/<xref ref-type="sec" rid="s12">Supplementary Material</xref>.</p>
</sec>
<sec id="s7">
<title>Ethics statement</title>
<p>The studies involving humans did not require additional ethical approval as the publicly available GWAS summary datasets were collected in studies that had already obtained the necessary ethical approvals. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin.</p>
</sec>
<sec id="s8">
<title>Author contributions</title>
<p>HoL: conceptualization, data curation, methodology, software, visualization, writing&#x2013;original draft, and writing&#x2013;review and editing. LZ: conceptualization, data curation, methodology, software, visualization, writing&#x2013;original draft, and writing&#x2013;review and editing. FY: data curation, software, visualization, and writing&#x2013;review and editing. XF: data curation and writing&#x2013;review and editing. RF: data curation, software, and writing&#x2013;review and editing. RZ: software and writing&#x2013;review and editing. XL: writing&#x2013;review and editing. HuL: conceptualization, data curation, formal analysis, investigation, supervision, visualization, and writing&#x2013;review and editing.</p>
</sec>
<sec id="s9">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The study was funded by the Natural Science Foundation of Shandong Province (ZR2021LZY029).</p>
</sec>
<sec sec-type="COI-statement" id="s10">
<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="s11">
<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="s12">
<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/fgene.2023.1269291/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2023.1269291/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table1.XLSX" id="SM1" mimetype="application/XLSX" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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