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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
<issn pub-type="epub">1664-3224</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2024.1402113</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Immunology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Plasma metabolites as mediators in immune cell-pancreatic cancer risk: insights from Mendelian randomization</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhang</surname>
<given-names>Ke</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2295727"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" equal-contrib="yes">
<name>
<surname>Zhu</surname>
<given-names>Jie</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn003">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2352343"/>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/data-curation/"/>
<role content-type="https://credit.niso.org/contributor-roles/investigation/"/>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Yuan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Zhengwang</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ge</surname>
<given-names>Xinyu</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wu</surname>
<given-names>Junqing</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Long</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lu</surname>
<given-names>Yipin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Xu</surname>
<given-names>Peng</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/conceptualization/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Yao</surname>
<given-names>Jie</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
</contrib>
</contrib-group>
<aff id="aff1">
<sup>1</sup>
<institution>Dalian Medical University</institution>, <addr-line>Dalian</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Hepatobiliary and Pancreatic Surgery, Northern Jiangsu People&#x2019;s Hospital Affiliated Yangzhou University</institution>, <addr-line>Yangzhou</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Annalisa Marcuzzi, University of Ferrara, Italy</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Katrin Richter, University of Giessen, Germany</p>
<p>Rishat Ruzi, First Affiliated Hospital of Xinjiang Medical University, China</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Peng Xu, <email xlink:href="mailto:pengxu1986@126.com">pengxu1986@126.com</email>; Jie Yao, <email xlink:href="mailto:dryaojie@yzu.edu.cn">dryaojie@yzu.edu.cn</email>
</p>
</fn>
<fn fn-type="equal" id="fn003">
<p>&#x2020;These authors have contributed equally to this work and share first authorship</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>12</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>15</volume>
<elocation-id>1402113</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>31</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2024 Zhang, Zhu, Wang, Chen, Wang, Ge, Wu, Chen, Lu, Xu and Yao</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Zhang, Zhu, Wang, Chen, Wang, Ge, Wu, Chen, Lu, Xu and Yao</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>Immune cells play a crucial role in the development and progression of pancreatic cancer, yet the causal relationship remains uncertain due to complex immune microenvironments and conflicting research findings. Mendelian randomization (MR), this study aims to delineate the causal relationships between immune cells and pancreatic cancer while identifying intermediary factors.</p>
</sec>
<sec>
<title>Methods</title>
<p>The genome-wide association study (GWAS) data on immune cells, pancreatic cancer, and plasma metabolites are derived from public databases. In this investigation, inverse variance weighting (IVW) as the primary analytical approach to investigate the causal relationship between exposure and outcome. Furthermore, this study incorporates MR-Egger, simple mode, weighted median, and weighted mode as supplementary analytical approaches. To ensure the reliability of our findings, we further assessed horizontal pleiotropy and heterogeneity and evaluated the stability of MR results using the Leave-one-out method. In conclusion, this study employed mediation analysis to elucidate the potential mediating effects of plasma metabolites.</p>
</sec>
<sec>
<title>Results</title>
<p>Our investigation revealed a causal relationship between immune cells and pancreatic cancer, highlighting the pivotal roles of CD11c+ monocytes (odds ratio, OR<sub>IVW</sub>=1.105; 95% confidence interval, 95%CI: 1.002&#x2013;1.218; P=0.045), HLA DR+ CD4+ antigen-presenting cells (OR<sub>IVW</sub>=0.920; 95%CI: 0.873&#x2013;0.968; P=0.001), and HLA DR+ CD8br T cells (OR<sub>IVW</sub>=1.058; 95%CI: 1.002&#x2013;1.117; P=0.041) in pancreatic cancer progression. Further mediation analysis indicated that oxalate (proportion of mediation effect in total effect: -11.6%, 95% CI: -89.7%, 66.6%) and the mannose to trans-4-hydroxyproline ratio (-19.4, 95% CI: -136%, 96.8%) partially mediate the relationship between HLA DR+ CD8br T cells and pancreatic cancer in nature. In addition, our analysis indicates that adrenate (-8.39%, 95% CI: -18.3%, 1.54%) plays a partial mediating role in the association between CD11c+ monocyte and pancreatic cancer, while cortisone (-26.6%, 95% CI: 138%, -84.8%) acts as a partial mediator between HLA DR+ CD4+ AC and pancreatic cancer.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>This MR investigation provides evidence supporting the causal relationship between immune cell and pancreatic cancer, with plasma metabolites serving as mediators. Identifying immune cell phenotypes with potential causal effects on pancreatic cancer sheds light on its underlying mechanisms and suggests novel therapeutic targets.</p>
</sec>
</abstract>
<kwd-group>
<kwd>pancreatic cancer</kwd>
<kwd>immune cells</kwd>
<kwd>Mendelian randomization</kwd>
<kwd>mediation analysis</kwd>
<kwd>plasma metabolites</kwd>
</kwd-group>
<counts>
<fig-count count="3"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="88"/>
<page-count count="11"/>
<word-count count="5309"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Cytokines and Soluble Mediators in Immunity</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<title>Introduction</title>
<p>As a frequently encountered malignant neoplasm, pancreatic cancer is characterized by its high malignancy and dismal prognosis (<xref ref-type="bibr" rid="B1">1</xref>). Over the last three decades, there has been a consistent rise in the incidence of pancreatic cancer. Projections indicate that by 2040, the overall incidence of pancreatic cancer is expected to surge by 30%. Pancreatic cancer typically presents as pancreatic adenocarcinoma, characterized by a grim prognosis, with an overall 5-year relative survival rate of approximately 10% (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>). In recent years, the ongoing development of immunotherapy has somewhat prolonged the overall survival of patients with advanced pancreatic cancer. Nevertheless, the prognosis remains challenging due to the intricacies of the immune microenvironment (<xref ref-type="bibr" rid="B4">4</xref>). Typically, risk factors associated with pancreatic cancer encompass unhealthy lifestyle behaviors, chronic pancreatitis, and a familial&#xa0;history of the disease (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B6">6</xref>). As mounting evidence suggests,&#xa0;immune dysregulation and remodeling of the immune microenvironment emerge as risk factors contributing to the onset and progression of pancreatic cancer (<xref ref-type="bibr" rid="B7">7</xref>&#x2013;<xref ref-type="bibr" rid="B9">9</xref>). Therefore, precise identification of immune-related risk factors and thorough exploration of their association with pancreatic cancer is paramount.</p>
<p>The immune microenvironment encompasses the complex milieu surrounding tumors, comprising cells, cytokines, extracellular matrix, blood vessels, and other factors. It exerts crucial regulatory influence over tumor growth and progression (<xref ref-type="bibr" rid="B10">10</xref>). As a complex regulatory network predominantly comprised of immune cells, the immune microenvironment demonstrates a&#xa0;dichotomous role in both anti-tumor and pro-tumor activities&#xa0;(<xref ref-type="bibr" rid="B11">11</xref>&#x2013;<xref ref-type="bibr" rid="B13">13</xref>). While the immune system can effectively eradicate&#xa0;tumor cells via tumor immunosurveillance, tumors eventually&#xa0;circumvent immune surveillance by orchestrating an immunosuppressive microenvironment (<xref ref-type="bibr" rid="B14">14</xref>). This underscores the crucial need to precisely identify potent anti-tumor immune cells within the immune microenvironment and restore their cytotoxic effects on tumors. Such efforts could significantly bolster the response rates to immunotherapy and foster the development of novel immunotherapeutic strategies. Currently, there have been no randomized controlled trials (RCTs) investigating the relationship between immune cells and pancreatic cancer. Consequently, the potential causal relationships between specific immune cells and pancreatic cancer remain unknown.</p>
<p>Besides the intricate immune microenvironment, the distinctive metabolic traits of pancreatic cancer play a crucial role in fostering tumor cell survival, proliferation, and metastasis (<xref ref-type="bibr" rid="B15">15</xref>, <xref ref-type="bibr" rid="B16">16</xref>). Previous studies have indicated that metabolic reprogramming in pancreatic cancer could induce changes in immune cell phenotypes, thereby&#xa0;fostering the establishment of suppressive immune microenvironments (<xref ref-type="bibr" rid="B17">17</xref>). Moreover, with the progress of metabolomics, an expanding array of plasma metabolites are being confirmed to play crucial roles in the early diagnosis of pancreatic cancer (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B19">19</xref>). Previous studies have indicated that alterations in tumor cell metabolism are partially driven by the recruitment of immune cells (<xref ref-type="bibr" rid="B20">20</xref>). For instance, research by Pham&#xa0;et&#xa0;al. demonstrated that macrophages in the tumor microenvironment are reprogrammed into a pro-tumor (M2) phenotype through the uptake of lactate, a byproduct of tumor cell glycolysis. This uptake further activates HIF-1&#x3b1;, leading to increased expression of arginase 1 (Arg1) (<xref ref-type="bibr" rid="B21">21</xref>). Additionally, Geiger et&#xa0;al. found that high levels of Arg1 in the tumor microenvironment are associated with lower anti-tumor activity (<xref ref-type="bibr" rid="B22">22</xref>). Subsequently, a growing body of research has revealed that aberrant metabolites or intermediates of cancer metabolism play a crucial role in regulating the proliferation, differentiation, activation, and function of immune cells (<xref ref-type="bibr" rid="B23">23</xref>&#x2013;<xref ref-type="bibr" rid="B25">25</xref>). For instance, in a study by Shane et&#xa0;al., elevated levels of tetrahydrobiopterin (BH4) were found to enhance the responses of CD4 and CD8 T cells, thereby amplifying their antitumor activity <italic>in vivo</italic>. Furthermore, BH4 administration in mice markedly reduced tumor growth and increased the population of intra-tumoral effector T cells (<xref ref-type="bibr" rid="B23">23</xref>). In a comprehensive metabolome-wide association study (MWAS), Zhong et&#xa0;al. systematically investigated the associations between genetically predicted blood metabolite levels and PDAC risk (<xref ref-type="bibr" rid="B26">26</xref>). Utilizing a two-sample MR approach, they identified 44 unique plasma metabolites that exhibit significant associations with PDAC risk (<xref ref-type="bibr" rid="B27">27</xref>). These findings indicate a potential causal relationship between plasma metabolites and the development, progression, and immune cell infiltration of tumors. Plasma metabolites may serve as intermediaries, mediating the causal association between immune cells and pancreatic cancer.</p>
<p>Despite the scarcity of RCTs evaluating the influence of plasma metabolites on immune cells and pancreatic cancer, it remains imperative to investigate the potential effects of plasma metabolites on both immune cells and pancreatic cancer.</p>
<p>MR provides a robust approach to investigate the causal relationship between exposure and outcomes by leveraging genetic variation, thereby mitigating confounding effects and reverse causation (<xref ref-type="bibr" rid="B28">28</xref>&#x2013;<xref ref-type="bibr" rid="B30">30</xref>). Hence, it is apparent that the MR approach shares a conceptual resemblance with RCTs, yet it boasts broader applicability and superior cost-effectiveness in practical implementation. To probe the potential causal link between immune cells and pancreatic cancer, as well as the intermediary role of plasma metabolites, we devised a &#x201c;two-step&#x201d; MR analysis. This approach delves into the causal nexus between immune cells and pancreatic cancer, along with the mediating influence of plasma metabolites. Our research offers a fresh perspective on the mechanisms driving the onset and progression of pancreatic cancer, potentially paving the way for innovative metabolic intervention strategies.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<title>Materials and methods</title>
<sec id="s2_1">
<title>Research design</title>
<p>Based on bidirectional MR analysis using two independent samples and mediation analysis, this study investigated the causal relationship between 731 immune cell phenotypes and pancreatic cancer, and explored the potential modulation of this relationship by circulating metabolites. (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>) At the outset, pancreatic cancer was designated as the primary outcome, while 731 immune cell phenotypes were identified as potential exposures, enabling an investigation into the comprehensive causal relationship between immune cell phenotypes and pancreatic cancer. Following this, we examined the respective proportions of causative links between 1400 plasma metabolites, acting as potential mediators, and the connections between pancreatic cancer and immune cell phenotypes. The instrumental variables (IV) employed in this investigation must satisfy the following criteria: 1. They must exhibit a strong correlation with the exposure factors. 2. The selected instrumental variables remain unaffected by confounding factors, and 3. They demonstrate a distinct relationship with the outcome event (<xref ref-type="bibr" rid="B29">29</xref>). (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>) Furthermore, the GWAS data employed in this investigation were sourced from publicly available datasets and received approval from the respective institutional review boards in their studies.</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>The mediation analysis X (exposure): immune cell; Y (outcome): pancreatic cancer; Z (mediator): plasma metabolites.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1402113-g001.tif"/>
</fig>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>Overview of study design: Three Fundamental Assumptions of MR Analysis.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1402113-g002.tif"/>
</fig>
</sec>
<sec id="s2_2">
<title>Source of data</title>
<sec id="s2_2_1">
<title>The origin of the pancreatic cancer GWAS dataset</title>
<p>The statistical summary data for pancreatic cancer GWAS originates from the FinnGen consortium (<ext-link ext-link-type="uri" xlink:href="https://www.finngen.fi/en">https://www.finngen.fi/en</ext-link>), a nationwide longitudinal cohort study that encompasses genetic and electronic health record data. We incorporated GWAS data from 1626 pancreatic cancer patients, in accordance with the ICD-10 diagnostic criteria, sourced from the R10 version of FinnGen.</p>
</sec>
<sec id="s2_2_2">
<title>The GWAS data for immune cell phenotypes</title>
<p>The GWAS data on immune cell phenotypes were obtained from a cohort study involving 3,757 individuals of European ancestry (accession numbers from ebi-a-GCST0001391 to ebi-a-GCST0002121) (<xref ref-type="bibr" rid="B31">31</xref>). The research investigated the correlation between 731 immune cell phenotypes, covering B cells, dendritic cells, various stages of T cells, monocytes, NK cells, and regulatory T cells. These phenotypes included 118 absolute cell counts (AC), 389 median fluorescence intensities (MFI) reflecting surface antigen levels, 32 morphological parameters (MP), and 192 relative cell counts (RC), and their associations with 22 million single nucleotide polymorphisms (SNPs).</p>
</sec>
<sec id="s2_2_3">
<title>The GWAS data for plasma metabolites</title>
<p>The plasma metabolite GWAS dataset was obtained from the Canadian Longitudinal Study on Aging (CLSA), a cohort study involving 8299 participants (<xref ref-type="bibr" rid="B32">32</xref>). In this study, a total of 1458 metabolites were quantified with the Metabolon HD4 platform and were then batch-normalized. Only those with missing measurements in fewer than 50% of samples (<inline-formula>
<mml:math display="inline" id="im1">
<mml:mrow>
<mml:mi>N</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>1091</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>) were retained (<xref ref-type="bibr" rid="B32">32</xref>). Furthermore, the study utilized the Human Metabolome Database (HMDB) to identify 309 pairs of metabolites that share enzymes or transporters (<inline-formula>
<mml:math display="inline" id="im2">
<mml:mrow>
<mml:mi>N</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>309</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>) (<xref ref-type="bibr" rid="B32">32</xref>, <xref ref-type="bibr" rid="B33">33</xref>). The ratio for each metabolite pair was then calculated by dividing the batch-normalized measurement value of one metabolite by the measurement value of the other metabolite within the same individual. Finally, a total of 1400 unique metabolites were evaluated in this study.</p>
</sec>
</sec>
<sec id="s2_3">
<title>Mendelian randomization analysis</title>
<sec id="s2_3_1">
<title>IVs screening</title>
<p>The assumptions regarding IVs entail a substantial correlation with the exposure factor, while maintaining independence from the outcome variable. Moreover, only the exposure factor is presumed to have a direct impact on the outcome. Therefore, in order to screen for IVs associated with the exposure factor, we identified SNPs linked to the exposure factor, with a significance level set at <inline-formula>
<mml:math display="inline" id="im3">
<mml:mrow>
<mml:mn>5</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:msup>
<mml:mrow>
<mml:mn>10</mml:mn>
</mml:mrow>
<mml:mrow>
<mml:mo>&#x2212;</mml:mo>
<mml:mn>8</mml:mn>
</mml:mrow>
</mml:msup>
</mml:mrow>
</mml:math>
</inline-formula>. Additionally, we further removed linkage disequilibrium (LD) effects for each SNP based on <inline-formula>
<mml:math display="inline" id="im4">
<mml:mrow>
<mml:mo>&#xa0;</mml:mo>
<mml:msup>
<mml:mi>&#x3b3;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msup>
<mml:mo>&lt;</mml:mo>
<mml:mn>0.001</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula> and an interval of 10,000 kb (<xref ref-type="bibr" rid="B34">34</xref>, <xref ref-type="bibr" rid="B35">35</xref>). Furthermore, to ensure the strong correlation between IVs and exposure factors, this study employed F-statistics to assess the strength of IVs. A causal inference was considered to have significant bias when the F-value was less than 10 (<xref ref-type="bibr" rid="B36">36</xref>). Finally, to control for the impact of confounding factors on instrumental variables, this study employed the Phenoscanner and R software packages, setting a <inline-formula>
<mml:math display="inline" id="im5">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mo>&lt;</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:msup>
<mml:mrow>
<mml:mn>10</mml:mn>
</mml:mrow>
<mml:mrow>
<mml:mo>&#x2212;</mml:mo>
<mml:mn>5</mml:mn>
</mml:mrow>
</mml:msup>
</mml:mrow>
</mml:math>
</inline-formula> to eliminate palindromic SNPs and incompatible SNPs. This process excluded closely related to the occurrence of pancreatic cancer [BMI, smoking, drinking, diabetes, and pancreatitis, etc (<xref ref-type="bibr" rid="B37">37</xref>).] ensuring that the resulting instrumental variables were suitable for subsequent analysis.</p>
</sec>
<sec id="s2_3_2">
<title>MR analysis</title>
<p>To investigate the causal effects of exposure factors on outcome events and ensure robust results, we conducted a two-sample MR analysis using five different methods: IVW, Weighted Median, Simple Mode, Weighted Mode, and MR-Egger. The respective advantages and limitations of these methods are summarized in <xref ref-type="table" rid="T1">
<bold>Table&#xa0;1</bold>
</xref>.</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Summary of five methods proposed for mendelian randomization.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Method</th>
<th valign="middle" align="center">Consistency<break/>assumption</th>
<th valign="middle" align="center">Advantages and limitations</th>
<th valign="middle" align="center">Reference</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">IVW</td>
<td valign="middle" align="center">All genetic variants are valid instruments, without pleiotropic effects</td>
<td valign="middle" align="left">Most efficient (greatest statistical power), sensitive to pleiotropy</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B38">38</xref>)</td>
</tr>
<tr>
<td valign="middle" align="center">MR-Egger</td>
<td valign="middle" align="center">InSIDE</td>
<td valign="middle" align="left">Capable of detecting and addressing pleiotropy, albeit less precise, more susceptible to outliers, and lower in power than IVW</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B39">39</xref>)</td>
</tr>
<tr>
<td valign="middle" align="center">Weighted Median</td>
<td valign="middle" align="center">Majority valid</td>
<td valign="middle" align="left">Robust to outliers, sensitive to addition/removal of genetic<break/>variants</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B40">40</xref>)</td>
</tr>
<tr>
<td valign="middle" align="center">Weighted Mode</td>
<td valign="middle" align="center">Plurality valid</td>
<td valign="middle" align="left">Robust to outliers, sensitive to bandwidth parameter and<break/>addition/removal of genetic variants, generally conservative</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B41">41</xref>)</td>
</tr>
<tr>
<td valign="middle" align="center">Simple Mode</td>
<td valign="middle" align="center">Plurality valid</td>
<td valign="middle" align="left">Simple to compute and provides a quick check on the robustness of the results, less robust than Weighted Mode</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B41">41</xref>)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>InSIDE is the Instrument Strength Independent of Direct Effect assumption.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec id="s2_4">
<title>The assessment of heterogeneity and horizontal pleiotropy</title>
<p>To maintain the validity of the independence and exclusivity assumptions, it is essential to ensure that IVs do not affect the outcome through factors unrelated to the exposure variable. Employing the MR-Egger intercept test to assess horizontal pleiotropy and ensure the robustness of the research findings. When the P-value of the MR-Egger intercept is less than 0.05, horizontal pleiotropy is deemed to be present; conversely, if the P-value is greater than 0.05, horizontal pleiotropy is considered to be absent (<xref ref-type="bibr" rid="B42">42</xref>). Employing Cochran&#x2019;s Q statistic along with its corresponding P-value provides a quantitative evaluation of the heterogeneity among the selected IVs (<xref ref-type="bibr" rid="B43">43</xref>). In a final step, we conducted a sensitivity analysis using the &#x201c;leave-one-out&#x201d; approach to assess the impact of each SNP on the outcomes of the MR analysis (<xref ref-type="bibr" rid="B44">44</xref>). Statistical analysis and visualization of the results were conducted using the R software packages &#x201c;TwoSample MR&#x201d;, &#x201c;Mendelian-Randomization&#x201d;, and &#x201c;ggplot2&#x201d;.</p>
</sec>
<sec id="s2_5">
<title>Analysis of the overall causal effect</title>
<p>To obtain the overall causal effect between immune cell phenotypes and pancreatic cancer, we executed a bidirectional two-sample MR analysis encompassing 731 immune cell phenotypes and their association with pancreatic cancer. All analyses were performed using R version 4.3.1 software (<ext-link ext-link-type="uri" xlink:href="http://www.Rproject.org">http://www.Rproject.org</ext-link>). In the absence of considering heterogeneity and horizontal pleiotropy, we employed the &#x201c;Mendelian Randomization&#x201d; R package to preliminarily evaluate the causal relationship between 731 immune cell phenotypes and pancreatic cancer using IVW (<xref ref-type="bibr" rid="B45">45</xref>). Moreover, this study employed MR-Egger regression analysis and weighted median-based analysis to further assess the reliability of the study findings (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B46">46</xref>).</p>
</sec>
<sec id="s2_6">
<title>Mediation analysis</title>
<p>Using a two-step MR approach, this study conducted mediation analysis to explore whether plasma metabolites mediate the causal pathway from immune cell phenotypes to pancreatic cancer outcomes. The overall effect of immune cell phenotypes on pancreatic cancer can be decomposed into the direct effect of immune cell phenotypes on pancreatic cancer and the indirect effect mediated by intermediates (<xref ref-type="bibr" rid="B47">47</xref>). In the first step of analysis, we computed the causal impact (Beta1) of immune cell phenotypes on the mediators. Following this, in the second step of analysis, we evaluated the causal impact (Beta2) of the mediators on pancreatic cancer. The proportion of mediated effect in the total effect was then obtained by calculating <inline-formula>
<mml:math display="inline" id="im6">
<mml:mrow>
<mml:mo>&#xa0;</mml:mo>
<mml:mi>R</mml:mi>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
<mml:mrow>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>0</mml:mn>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:math>
</inline-formula>. The direct impact of the exposure on the outcome is represented as <inline-formula>
<mml:math display="inline" id="im7">
<mml:mrow>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>3</mml:mn>
<mml:mo>=</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>0</mml:mn>
<mml:mo>&#x2212;</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#xd7;</mml:mo>
<mml:mi>B</mml:mi>
<mml:mi>e</mml:mi>
<mml:mi>t</mml:mi>
<mml:mi>a</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>.</p>
</sec>
</sec>
<sec id="s3" sec-type="results">
<title>Results</title>
<sec id="s3_1">
<title>Exploring the overall causal impact of immune cell phenotypes on pancreatic cancer</title>
<sec id="s3_1_1">
<title>MR analysis</title>
<p>In order to investigate the overall causal impact of immune cell phenotypes on pancreatic cancer, we conducted two-sample MR analyses using five methods: IVW, weighted median, simple mode, weighted mode, and MR Egger. Subsequently, we employed a threshold of <inline-formula>
<mml:math display="inline" id="im8">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo>&lt;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula> derived from the IVW method to detect 27 immune cell phenotypes causally associated with pancreatic cancer. Among these, 16 immune cell phenotypes, including CD11c+ monocyte % monocyte, are regarded as risk factors for pancreatic cancer (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;1</bold>
</xref>), while 11 immune cell phenotypes, such as CD4 Treg AC are regarded as protective factors for pancreatic cancer (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;2</bold>
</xref>). The summarized results of the MR analysis are provided in <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Tables&#xa0;1</bold>
</xref>, <xref ref-type="supplementary-material" rid="SM1">
<bold>2</bold>
</xref>.</p>
</sec>
<sec id="s3_1_2">
<title>Analysis for horizontal pleiotropy and heterogeneity</title>
<p>To mitigate potential heterogeneity stemming from varying analysis platforms, experiments, populations, and other factors that may affect the results of MR analysis.</p>
<p>We evaluated heterogeneity using both the IVW and MR-Egger test methods, with <inline-formula>
<mml:math display="inline" id="im9">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo>&lt;</mml:mo>
<mml:mn>0.05</mml:mn>
<mml:mo>&#xa0;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> indicating the presence of heterogeneity in the study (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;4</bold>
</xref>). The results revealed that none of the intercepts from MR-Egger and IVW for the 27 immune cell phenotypes mentioned above were not statistically significant, indicating that the findings remained unaffected by any potential bias stemming from heterogeneity (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;3</bold>
</xref>). Furthermore, in order to mitigate potential confounding effects of IVs on outcomes via pathways beyond exposure, thereby safeguarding the assumptions of independence and exclusivity, we additionally employed the MR-Egger intercept method to assess the presence of horizontal pleiotropy in the data. The study findings indicate that the MR-Egger intercept for CX3CR1 on CD14- CD16- (<inline-formula>
<mml:math display="inline" id="im10">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>0.009772856</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>) exhibited horizontal pleiotropy and was consequently excluded from subsequent analyses (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;3</bold>
</xref>). The MR-Egger intercepts for the remaining 26 immune cell phenotypes (<inline-formula>
<mml:math display="inline" id="im11">
<mml:mrow>
<mml:mi>P</mml:mi>
<mml:mo>&gt;</mml:mo>
<mml:mn>0.05</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>) were not statistically significant, suggesting an absence of genetic pleiotropy bias. Through leave-one-out sensitivity analysis, the study results were further validated, revealing no indication of SNPs impacting the overall causal relationship (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;4</bold>
</xref>).</p>
</sec>
</sec>
<sec id="s3_2">
<title>Identifying immune cell phenotypes associated with pancreatic cancer</title>
<p>Based on the study findings, in order to identify immune cell phenotypes associated with pancreatic cancer for further analysis, we initially screened for immune cell phenotypes showing consistent directional effects (<inline-formula>
<mml:math display="inline" id="im12">
<mml:mrow>
<mml:mi>O</mml:mi>
<mml:mi>R</mml:mi>
<mml:mrow>
<mml:mo>&#x2329;</mml:mo>
<mml:mrow>
<mml:mn>1</mml:mn>
<mml:mo>&#xa0;</mml:mo>
<mml:mi>o</mml:mi>
<mml:mi>r</mml:mi>
<mml:mo>&#xa0;</mml:mo>
<mml:mi>O</mml:mi>
<mml:mi>R</mml:mi>
</mml:mrow>
<mml:mo>&#x232a;</mml:mo>
</mml:mrow>
<mml:mn>1</mml:mn>
</mml:mrow>
</mml:math>
</inline-formula>) across five different MR analysis methods. This screening process resulted in the identification of 22 immune cell phenotypes (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Figure&#xa0;5</bold>
</xref>). Subsequently, we conducted reverse MR analysis on the 22 immune cell phenotypes in relation to pancreatic cancer, excluding those with reverse causal effects (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;5</bold>
</xref>). Ultimately, we identified 20 immune cell phenotypes associated with pancreatic cancer for further mediation analysis.</p>
</sec>
<sec id="s3_3">
<title>Mediating effects of plasma metabolites on immune cell&#x2212;pancreatic cancer risk</title>
<p>To investigate the mediating effect of plasma metabolites on the association between immune cell and pancreatic cancer, with the aim of elucidating the underlying mechanisms by which immune cells influence pancreatic cancer. Based on two-step MR analysis, we first performed two-sample MR analysis on 1400 plasma metabolites with pancreatic cancer and then, plasma metabolites with consistent OR values were selected as pancreatic cancer-associated plasma metabolites for further analysis (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;6</bold>
</xref>).</p>
<p>In our investigation, we first examined the mediating effect (Beta1) of immune cell phenotype on plasma metabolites (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;7</bold>
</xref>). Subsequently, employing the same methodology, we analyzed the mediating effect (Beta2) of plasma metabolites on pancreatic cancer (<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Table&#xa0;8</bold>
</xref>). Ultimately, our analysis identified four pairs of plasma metabolites that serve as mediators in the relationship between immune cell phenotype and pancreatic cancer (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3</bold>
</xref>).</p>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>A forest plot of the four sets of plasma metabolites exhibiting &#x201c;immune cell phenotype - pancreatic cancer&#x201d; mediating effects nsnp, number of single nucleotide polymorphism; pval, p-value; OR, odds ratio. <bold>(A)</bold> CD11c+ monocyte % monocyte-Adrenate (22:4n6)-Pancreatic cancer. <bold>(B)</bold> HLA DR+ CD4+ AC-Cortisone-Pancreatic cancer. <bold>(C)</bold> HLA DR+ CD8br %T cell-Oxalate (ethanedioate)-Pancreatic cancer. <bold>(D)</bold> HLA DR+ CD8br %T cell-Mannose to trans-4-hydroxyproline ratio-Pancreatic cancer.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fimmu-15-1402113-g003.tif"/>
</fig>
<p>The levels of adrenate (22:4n6) were found to negatively modulate pancreatic cancer concerning CD11c+ monocyte %monocyte (Mediated effect, ME=-0.00837; Mediated proportion, MP=-8.39%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Oxalate (ethanedioate) levels exhibited a negative regulatory effect on pancreatic cancer with respect to HLA DR+ CD8br %T cell (ME=-0.00655; MP=-11.6%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Cortisone levels negatively regulated pancreatic cancer in conjunction with HLA DR+ CD4+ AC (ME=-0.0223; MP=-26.6%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Lastly, the mannose to trans-4-hydroxyproline ratio negatively influenced pancreatic cancer in association with HLA DR+ CD8br %T cell (ME=-0.011; MP=-19.4%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Four pairs of plasma metabolites with &#x201c;immune cell phenotype - pancreatic cancer&#x201d; mediating effects.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="middle" align="center">Immune cell</th>
<th valign="middle" align="center">Metabolite</th>
<th valign="middle" align="center">Outcome</th>
<th valign="middle" align="center">Mediated effect</th>
<th valign="middle" align="center">Mediated proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td valign="middle" align="center">CD11c+ monocyte %monocyte</td>
<td valign="middle" align="center">Adrenate (22:4n6) levels</td>
<td valign="middle" align="center">Pancreatic cancer</td>
<td valign="middle" align="center">-0.00837<break/>(-0.0183, 0.00153)</td>
<td valign="middle" align="center">-8.39%<break/>(-18.3%, 1.54%)</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ CD4+ AC</td>
<td valign="middle" align="center">Cortisone levels</td>
<td valign="middle" align="center">Pancreatic cancer</td>
<td valign="middle" align="center">-0.0223<break/>(-0.116, 0.0712)</td>
<td valign="middle" align="center">-26.6%<break/>(138%, -84.8%)</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ CD8br %<break/>T cell</td>
<td valign="middle" align="center">Oxalate (ethanedioate) levels</td>
<td valign="middle" align="center">Pancreatic cancer</td>
<td valign="middle" align="center">-0.00655<break/>(-0.0508, 0.0377)</td>
<td valign="middle" align="center">-11.6%<break/>(-89.7%, 66.6%)</td>
</tr>
<tr>
<td valign="middle" align="center">HLA DR+ CD8br %<break/>T cell</td>
<td valign="middle" align="center">Mannose to trans-4-hydroxyproline ratio</td>
<td valign="middle" align="center">Pancreatic cancer</td>
<td valign="middle" align="center">-0.011<break/>(-0.0768, 0.0548)</td>
<td valign="middle" align="center">-19.4%<break/>(-136%, 96.8%)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<title>Discussion</title>
<p>Pancreatic cancer, characterized by its high malignancy, aggressive biological behavior, and poor prognosis, presents a persistent threat to global health (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B48">48</xref>). Immune cells play a crucial role in the initiation and progression of pancreatic cancer (<xref ref-type="bibr" rid="B49">49</xref>). Despite the growing interest in immune cells and immunotherapy in pancreatic cancer research, the intricate dynamics between immune cells and pancreatic cancer remain largely unexplored. Metabolic reprogramming, as a pivotal biological phenomenon in tumor tissue, exerts a profound influence on tumor cell growth, proliferation, and survival (<xref ref-type="bibr" rid="B50">50</xref>).</p>
<p>Emerging evidence suggests that intricate metabolic alterations within the tumor microenvironment activate immune cells via diverse intrinsic or extrinsic pathways. These complex interactions are pivotal in driving multiple phenotypes such as tumor proliferation, migration, and drug resistance (<xref ref-type="bibr" rid="B51">51</xref>&#x2013;<xref ref-type="bibr" rid="B53">53</xref>). Therefore, the reciprocal interplay between immune cells and metabolism emerges as an intriguing topic warranting in-depth exploration. Nevertheless, due to the relatively small number of pancreatic cancer samples available, acquiring high-quality tissue specimens remains a formidable challenge. Furthermore, the complexity of metabolic products <italic>in vivo</italic> imposes limitations on the depth and breadth of research endeavors. Moreover, the absence of standardized criteria for investigating immune cell phenotypes has contributed to challenges in comparing and verifying findings. Therefore, in order to clarify whether immune cells impact the development of pancreatic cancer by mediating endogenous metabolites, we employed a two-step MR analysis to investigate whether plasma metabolites act as mediating factors affecting the causal relationship between immune cells and pancreatic cancer. The outcomes of our investigation reveal a causal relationship between immune cells and pancreatic cancer. Furthermore, mediation analysis provides additional evidence supporting the mediating effect of plasma metabolites in the causal relationship between immune cells and pancreatic cancer. This discovery may provide fresh insights into the mechanisms driving the onset and progression of pancreatic cancer, thereby offering novel intervention targets for therapeutic strategies.</p>
<p>Adrenate (22:4n6) is found to negatively mediate the causal relationship between CD11c+ monocyte and pancreatic cancer (ME=-0.00837, MP=-8.39%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Recent investigations have revealed the significant involvement of CD11c+ monocytes in the early metastatic process of pancreatic cancer (<xref ref-type="bibr" rid="B54">54</xref>), aligning with our own research findings (OR<sub>IVW</sub>=1.105, 95%CI=1.002&#x2013;1.218) (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). Adrenate (22:4n6), a 22-carbon, 4-double-bond &#x3c9;-6 fatty acid also known as adrenic acid, plays a crucial biological role in the human body. It contributes to various essential functions, including the structural and functional integrity of cellular membranes, cell signaling processes, and the regulation of inflammation (<xref ref-type="bibr" rid="B55">55</xref>). Research findings indicate that its peroxidation plays a critical role in regulating the sensitivity of various drug-resistant tumor cells to ferroptosis (<xref ref-type="bibr" rid="B56">56</xref>, <xref ref-type="bibr" rid="B57">57</xref>). The observations indicate that adrenate (22:4n6) exhibits anti-tumor effects, consistent with the outcomes of our study (OR<sub>IVW</sub>=0.886, 95%CI=0.820&#x2013;0.958) (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). In a study of murine peritonitis, adrenate (22:4n6) was found to suppress leukotriene B4 (LBT4) synthesis in neutrophils and promote their apoptosis, thereby facilitating phagocytosis by monocyte-derived macrophages and ultimately exerting an anti-inflammatory effect (<xref ref-type="bibr" rid="B58">58</xref>). The findings above suggest that adrenate (22:4n6) may act as a mediator, mitigating local inflammatory responses within the tumor microenvironment by inhibiting leukotriene synthesis in immune cells, thereby modulating immune cell effects on pancreatic cancer.</p>
<p>HLA-DR, a key component of the major histocompatibility complex (MHC) class II molecules, is commonly found on specialized antigen-presenting cells. It binds external peptides and delivers them to antigen-specific CD4+ helper (Th) T lymphocytes (<xref ref-type="bibr" rid="B59">59</xref>). Research findings suggest that HLA-DR+ CD4+ T lymphocytes play a critical role as activated T cells in anti-tumor immune responses (<xref ref-type="bibr" rid="B60">60</xref>). They regulate and enhance the activities of other immune cells while also directly targeting tumor cells for cytotoxic effects (<xref ref-type="bibr" rid="B61">61</xref>). Our study supports this viewpoint, suggesting an inverse correlation between HLA-DR+ CD4+ antigen-presenting cells and pancreatic cancer (OR<sub>IVW</sub>=0.920, 95%CI=0.873&#x2013;0.968) (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>). Further mediation analysis revealed that cortisone exerts a negative mediating effect (ME=-0.0223, MP=-26.6%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>). Cortisone, a natural glucocorticoid present in the human body, plays a critical role in maintaining metabolic balance, regulating cell growth, and modulating inflammatory responses (<xref ref-type="bibr" rid="B62">62</xref>). Research has uncovered that cortisone binding to glucocorticoid receptors (GR) in pancreatic cancer elicits tumor proliferation, metastasis, and gemcitabine resistance via diverse signaling cascades (<xref ref-type="bibr" rid="B63">63</xref>). Cortisol, the most representative hormone among glucocorticoids, is commonly used to treat inflammatory diseases. Traditionally, exogenous glucocorticoids (GCs) have been widely recognized for their immunosuppressive effects (<xref ref-type="bibr" rid="B62">62</xref>). However, recent studies on endogenous GCs have revealed that they can both promote and inhibit T cell immunity (<xref ref-type="bibr" rid="B64">64</xref>). For instance, current research indicates that endogenous GCs induce differential T cell differentiation to maintain a balance, thereby preventing excessive immune responses (<xref ref-type="bibr" rid="B65">65</xref>). The results reported in the previous study are consistent with our own findings (OR<sub>IVW</sub>=1.458, 95%CI=1.138&#x2013;1.868) (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3B</bold>
</xref>), indicating that cortisone acts as a mediator, attenuating the impact of HLA DR+ CD4+ T lymphocytes on pancreatic cancer.</p>
<p>HLA DR+ CD8br T cells are commonly acknowledged as activated cytotoxic T lymphocytes (CTLs), with the conventional viewpoint asserting their significance as a key element in cellular immunity, playing a crucial role in the antitumor process (<xref ref-type="bibr" rid="B66">66</xref>). Nevertheless, recent investigations have revealed that excessive activation of CTLs could foster the development of an immunosuppressive microenvironment. In Yang et&#xa0;al.&#x2019;s investigation, it was observed that in lung adenocarcinoma, hyperactivated CTLs induce tumor cells to produce prostaglandin E2 (PGE2) and recruit myeloid-derived suppressor cells (MDSCs) via the Fas/FasL signaling axis, thereby fostering tumor immune evasion (<xref ref-type="bibr" rid="B67">67</xref>). The above viewpoint is consistent with the findings of this study (OR<sub>IVW</sub>=1.058, 95%CI=1.002&#x2013;1.117) (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3C</bold>
</xref>, <xref ref-type="fig" rid="f3">
<bold>3D</bold>
</xref>). Further mediation analysis revealed the participation of two plasma metabolites in mediating the causal effect of HLA DR+ CD8br T cells on pancreatic cancer. Intracellular accumulation of oxalate, a passive metabolite synthesized by various cells (<xref ref-type="bibr" rid="B68">68</xref>), has been found to activate nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, thereby inducing reactive oxygen species (ROS)-mediated oxidative stress (OS) and subsequent tissue damage (<xref ref-type="bibr" rid="B69">69</xref>, <xref ref-type="bibr" rid="B70">70</xref>). In light of OS emerging as a pivotal biological phenomenon in tumor progression, the dual therapeutic strategy aimed at modulating the redox status (i.e., pro-oxidant therapy and/or antioxidant therapy) is gaining increasing attention (<xref ref-type="bibr" rid="B71">71</xref>, <xref ref-type="bibr" rid="B72">72</xref>). OS acts as a double-edged sword in tumor biology. On one hand, it promotes malignant behaviors in tumors, while on the other hand, it triggers cytotoxic OS within tumor cells, which can be exploited for therapeutic purposes (<xref ref-type="bibr" rid="B73">73</xref>, <xref ref-type="bibr" rid="B74">74</xref>). For instance, commonly used chemotherapy drugs like doxorubicin, cisplatin, and 5-fluorouracil can either directly or indirectly enhance the accumulation of ROS, leading to tumor cell death (<xref ref-type="bibr" rid="B75">75</xref>, <xref ref-type="bibr" rid="B76">76</xref>). A cohort study on dialysis patients revealed a significant elevation in IL-16 levels, which correlated with plasma oxalate concentrations (<xref ref-type="bibr" rid="B77">77</xref>). Similarly, research on colorectal cancer indicated that high IL-16 levels inhibited the cytotoxicity of CD8+ T cells in the immune microenvironment (<xref ref-type="bibr" rid="B78">78</xref>). These findings suggest that plasma oxalate may impair CD8+ T cell cytotoxicity through the upregulation of cytokines like IL-16.</p>
<p>In summary, oxalate might trigger apoptosis in tumor cells through OS, consequently attenuating the positive causal relationship between HLA DR+ CD8br T cells and pancreatic cancer.</p>
<p>Intracellular uptake of mannose, a natural C-2 epimer of glucose, occurs via facilitated diffusion through glucose transporters (GLUTs) situated on the cell membrane (<xref ref-type="bibr" rid="B79">79</xref>). Subsequently, within the cell, mannose undergoes enzymatic conversion catalyzed by phosphomannose isomerase (MPI) to yield fructose-6-phosphate, thereby entering the glycolytic pathway (<xref ref-type="bibr" rid="B80">80</xref>). However, the limited endogenous levels of mannose result in minimal energy-providing capacity. In recent years, research has shown that the combination of mannose with conventional chemotherapy impacts the levels of Bcl-2 family anti-apoptotic proteins, resulting in increased sensitivity of cells to chemotherapy drugs (<xref ref-type="bibr" rid="B81">81</xref>). In addition to its direct anti-tumor effects, mannose also possesses immunomodulatory properties. In a study conducted by Zhang et&#xa0;al., it was found that increasing the dosage of mannose in the drinking water of mice suppressed immune cell infiltration in autoimmune diabetes and airway inflammation, while enhancing the infiltration of regulatory T cells (Tregs) (<xref ref-type="bibr" rid="B82">82</xref>). Further <italic>in vitro</italic> experiments confirmed that mannose stimulates the differentiation of Tregs in both humans and mice by promoting the activation of TGF-&#x3b2; (<xref ref-type="bibr" rid="B82">82</xref>, <xref ref-type="bibr" rid="B83">83</xref>). In a separate investigation focusing on mannose, it was revealed that supra-physiological concentrations of mannose can inhibit macrophage activation (<xref ref-type="bibr" rid="B84">84</xref>). From this perspective, mannose not only exerts direct anti-tumor effects but also demonstrates negative immunomodulatory effects.</p>
<p>Hydroxyproline, an essential structural amino acid in the body, plays a crucial role in collagen synthesis. Studies have indicated that an increase in hydroxyproline concentration in the blood promotes the distant metastasis of pancreatic cancer (<xref ref-type="bibr" rid="B85">85</xref>). Moreover, studies have demonstrated that hydroxyproline enhances IFN-&#x3b3; levels, thereby promoting the expression of PD-L1 and inhibiting tumor autophagy (<xref ref-type="bibr" rid="B86">86</xref>).</p>
<p>The specific role of trans-4-hydroxyproline, a variant of hydroxyproline, in cancer pathogenesis remains largely elusive. A large-scale prospective study on plasma metabolites in prostate cancer identified a significant association between elevated plasma levels of trans-4-hydroxyproline and heightened risk of prostate cancer (<xref ref-type="bibr" rid="B87">87</xref>). In summary, further investigations are needed to fully elucidate the specific roles of mannose and trans-4-hydroxyproline in tumorigenesis. Our study highlights the mannose to trans-4-hydroxyproline ratio as a risk factor, exerting a negative regulatory effect on HLA DR+ CD8br % T cell responses in pancreatic cancer (ME=-0.011, MP=-19.4%) (<xref ref-type="table" rid="T2">
<bold>Table&#xa0;2</bold>
</xref>).</p>
<p>One of the major strengths of our study lies in the application of MR, which employs SNPs as IVs to analyze the relationship between exposure and outcome. To our knowledge, this study represents the first attempt to employ MR analysis using the most recent and comprehensive GWAS data to investigate the causal relationship between immune cells and pancreatic cancer. Compared to RCTs, MR helps mitigate confounding factors and prevents interference from reverse causality. We employ two-steps MR to investigate the linear relationship between exposure and outcome, and employ mediation analysis to explore potential non-linear associations of plasma metabolites. Furthermore, the utilization of data samples derived from the identical population minimizes biases originating from population heterogeneity. Conducting multiple sensitivity analyses enhances the resilience and dependability of the outcomes. Nevertheless, our study still faces several noteworthy limitations. Firstly, the bulk of statistical data in GWAS originates from individuals of European descent, limiting the generalizability of our findings to diverse ethnic populations. Consequently, additional research is needed to extend our results to other racial groups. Secondly, regarding the selection of outcome GWAS data, this study utilized GWAS data from FinnGen. Although the FinnGen dataset is smaller in scale compared to the larger GWAS datasets from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), FinnGen, which commenced in the fall of 2017, offers an advantage in terms of data timeliness (<xref ref-type="bibr" rid="B88">88</xref>). Additionally, Zhong et&#xa0;al. have already analyzed the association of plasma metabolites with pancreatic cancer using PanScan and PanC4 data (<xref ref-type="bibr" rid="B26">26</xref>, <xref ref-type="bibr" rid="B27">27</xref>). Our study aims to explore different GWAS datasets to identify new potential mediators, thereby providing complementary insights and enhancing the robustness of our findings. Thirdly, the causal relationship between immune cells and pancreatic cancer is influenced by numerous factors, and there may still be unidentified confounding variables between exposure and outcome, which could introduce bias into our results.</p>
</sec>
<sec id="s5" sec-type="conclusions">
<title>Conclusion</title>
<p>In summary, this study presents genetic evidence of the causal relationship between immune cells and pancreatic cancer through MR analysis. In our investigation, we found that higher levels of HLA-DR+ CD4+ T lymphocytes were associated with a decreased risk of pancreatic cancer. Notably, cortisone was found to significantly modulate this effect, explaining 26.6% of the association. Furthermore, the investigation revealed a direct association between HLA DR+ CD8 T cells and pancreatic cancer. Notably, the mannose to trans-4-hydroxyproline ratio emerged as a key mediator, explaining 19.4% of the total effect. Additionally, oxalate showed significant mediation, accounting for 11.6% of the total effect. In essence, higher levels of HLA DR+ CD8 T cells corresponded to an increased risk of pancreatic cancer, with the mannose to trans-4-hydroxyproline ratio and oxalate serving as intermediaries. In addition, the findings revealed a positive correlation between CD11c+ monocytes and pancreatic cancer incidence, with adrenate serving as a mediator, accounting for 8.39% of the effect. In conclusion, the findings from the above study may offer fresh insights into the mechanisms underlying the initiation and advancement of pancreatic cancer, thus presenting novel metabolic intervention targets for therapeutic strategies.</p>
</sec>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found here: <uri xlink:href="https://www.finngen.fi/en">https://www.finngen.fi/en</uri>.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Ethical approval was deemed unnecessary for the human studies as the original studies had undergone ethical review and approval, as documented in their respective publications. Informed consent was obtained from all participants involved in the original genome-wide association studies. The studies were conducted in accordance with local legislation and institutional requirements. Participants provided written informed consent to participate in this study.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>KZ: Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. JZ: Conceptualization, Data curation, Investigation, Validation, Writing &#x2013; review &amp; editing. PW: Writing &#x2013; review &amp; editing. YC: Writing &#x2013; review &amp; editing. ZW: Writing &#x2013; review &amp; editing. XG: Writing &#x2013; review &amp; editing. JW: Writing &#x2013; review &amp; editing. LC: Writing &#x2013; review &amp; editing. YL: Writing &#x2013; review &amp; editing. PX: Conceptualization, Methodology, Writing &#x2013; review &amp; editing. JY: Writing &#x2013; review &amp; editing.</p>
</sec>
</body>
<back>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.</p>
</sec>
<ack>
<title>Acknowledgments</title>
<p>The authors express their sincere gratitude to the researchers who generously provided the original dataset used in this study.</p>
</ack>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s12" 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/fimmu.2024.1402113/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2024.1402113/full#supplementary-material</ext-link>
</p>
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<supplementary-material xlink:href="DataSheet_5.docx" id="SM5" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document"/>
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