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<front>
<journal-meta>
<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>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">757897</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2021.757897</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>Pan-Cancer Analyses Reveal Oncogenic and Immunological Role of Dickkopf-1 (DKK1)</article-title>
<alt-title alt-title-type="left-running-head">Gao et&#x20;al.</alt-title>
<alt-title alt-title-type="right-running-head">Pan-Cancer Analysis of DKK1</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Gao</surname>
<given-names>Shuang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Jin</surname>
<given-names>Ye</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1539094/overview"/>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Zhang</surname>
<given-names>Hongmei</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1430419/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<label>
<sup>1</sup>
</label>College of Life Science, North China University of Science and Technology, <addr-line>Tangshan</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<label>
<sup>2</sup>
</label>School of Public Health, North China University of Science and Technology, <addr-line>Tangshan</addr-line>, <country>China</country>
</aff>
<aff id="aff3">
<label>
<sup>3</sup>
</label>School of Clinical Medicine, North China University of Science and Technology, <addr-line>Tangshan</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<label>
<sup>4</sup>
</label>Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, <addr-line>Tangshan</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/89738/overview">Alfredo Pulvirenti</ext-link>, University of Catania, Italy</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/864169/overview">Hongde Liu</ext-link>, Southeast University, China</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/459395/overview">Qi Zhao</ext-link>, University of Science and Technology Liaoning, China</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Hongmei Zhang, <email>zhanghm@ncst.edu.cn</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>11</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>12</volume>
<elocation-id>757897</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>08</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>05</day>
<month>11</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2021 Gao, Jin and Zhang.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Gao, Jin and Zhang</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&#x20;terms.</p>
</license>
</permissions>
<abstract>
<p>WNT signaling pathway inhibitor Dickkopf-1 (<italic>DKK1</italic>) is related to cancer progression; however, its diagnostic and prognostic potential have not been investigated in a pan-cancer perspective. In this study, multiple bioinformatic analyses were conducted to evaluate therapeutic value of <italic>DKK1</italic> in human cancers. The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project served as data resources. The Wilcoxon rank test was performed to evaluate the expression difference of <italic>DKK1</italic> between cancer tissues and normal tissues. A Kaplan-Meier curve and Cox regression were used for prognosis evaluation. Single-sample gene set enrichment analysis (ssGSEA) was used to evaluate the association of DKK1 expression with the immune cell infiltration. The potential function of DKK1 was explored by STRING and clusterProfiler. We found that the expression level of <italic>DKK</italic>1 is significantly different in different cancer types. Importantly, we demonstrated that <italic>DKK1</italic> is an independent risk factor in ESCA, LUAD, MESO, and STAD. Further analysis revealed that <italic>DKK1</italic> had a large effect on the immune cell infiltration and markers of certain immune cells, such as Th1 and Th2 cells. PPI network analysis and further pathway enrichment analysis indicated that <italic>DKK1</italic> was mainly involved in the WNT signaling pathway. Our findings suggested that <italic>DKK1</italic> might serve as a marker of prognosis for certain cancers by affecting the WNT signaling pathway and tumor immune microenvironment.</p>
</abstract>
<kwd-group>
<kwd>DKK1</kwd>
<kwd>pan-cancer</kwd>
<kwd>survival analysis</kwd>
<kwd>immune infiltration</kwd>
<kwd>biomarker</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>Worldwide, malignant tumors have jeopardized public health (<xref ref-type="bibr" rid="B42">Siegel et&#x20;al., 2021</xref>). WNT signaling plays a critical role in the progress of multiple cancer types (<xref ref-type="bibr" rid="B7">Bian et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B34">Peng et&#x20;al., 2021</xref>; <xref ref-type="bibr" rid="B44">Sun et&#x20;al., 2021</xref>). Aberrant WNT signaling may subvert cancer immunosurveillance (<xref ref-type="bibr" rid="B43">Spranger and Gajewski, 2015</xref>; <xref ref-type="bibr" rid="B3">Augustin et&#x20;al., 2016</xref>; <xref ref-type="bibr" rid="B18">Hong et&#x20;al., 2016</xref>). Dickkopf-1 (<italic>DKK1</italic>), as a WNT signaling pathway inhibitor, is involved in the development of several types of cancers (<xref ref-type="bibr" rid="B30">Lu et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B53">Zhuang et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B19">Igbinigie et&#x20;al., 2019</xref>). <italic>DKK1</italic> had decreased expression in both gastric cancer (GC) and colorectal cancer (CRC), but increased expression in breast cancer (BRCA) and non-small cell lung cancer (NSCLC) (<xref ref-type="bibr" rid="B1">Aguilera et&#x20;al., 2006</xref>; <xref ref-type="bibr" rid="B38">Sato et&#x20;al., 2007</xref>; <xref ref-type="bibr" rid="B27">Li et&#x20;al., 2013</xref>; <xref ref-type="bibr" rid="B20">Jia et&#x20;al., 2016</xref>; <xref ref-type="bibr" rid="B22">Kasoha et&#x20;al., 2018</xref>). In esophageal cancer (ESCA), <italic>DKK1</italic> promoted cell proliferation through the cytoskeleton-associated protein 4 (<italic>CKAP4</italic>)-related pathway (<xref ref-type="bibr" rid="B41">Shinno et&#x20;al., 2018</xref>). <italic>DKK1</italic> was also involved in the invasion and metastasis of intrahepatic cholangiocarcinoma (ICC) cells and lymph node metastasis (<xref ref-type="bibr" rid="B40">Shi et&#x20;al., 2013</xref>). Various studies also demonstrated the effect of <italic>DKK1</italic> on the prognosis of certain cancers, such as head and neck squamous carcinoma (HNSC), NSCLC, and pancreatic adenocarcinoma (PAAD) (<xref ref-type="bibr" rid="B49">Yamabuki et&#x20;al., 2007</xref>; <xref ref-type="bibr" rid="B16">Han et&#x20;al., 2015</xref>; <xref ref-type="bibr" rid="B13">Gao et&#x20;al., 2018</xref>). In liver hepatocellular carcinoma (LIHC), <italic>DKK1</italic> could be induced by an active WNT/&#x3b2;-catenin signal and further contributed to patient&#x2019;s poor prognosis (<xref ref-type="bibr" rid="B50">Yu et&#x20;al., 2009</xref>). Some reports also presented that <italic>DKK1</italic> might serve as a target for immunotherapy (<xref ref-type="bibr" rid="B35">Qian et&#x20;al., 2012</xref>; <xref ref-type="bibr" rid="B5">Betella et&#x20;al., 2020</xref>). <italic>DKK1</italic> could affect the function of immune cells, such as T lymphocytes and bone marrow-derived suppressor cells (<xref ref-type="bibr" rid="B23">Katoh and Katoh, 2017</xref>). By activating CD4<sup>&#x2b;</sup> and CD8<sup>&#x2b;</sup> T lymphocytes, <italic>DKK1</italic> could eliminate myeloma cells in mouse models (<xref ref-type="bibr" rid="B35">Qian et&#x20;al., 2012</xref>). <italic>DKK1</italic> also inhibited the secretion of IFN-&#x3b3; in Th1 cells and induced the production of interleukin (IL)-4, IL-5, IL-10, and IL-13 in Th2 cells (<xref ref-type="bibr" rid="B4">Bais et&#x20;al., 2005</xref>). The inflammation caused by tumor-specific Th1 cells could prevent cancer, but Th2 cells have the opposite function (<xref ref-type="bibr" rid="B24">Kennedy and Celis, 2008</xref>; <xref ref-type="bibr" rid="B26">Lefrancois et&#x20;al., 2020</xref>). By inhibiting &#x3b2;-catenin to prevent clearance by natural killer (NK) cells, <italic>DKK1</italic> helps to sustain the stem cell-like properties of cancer cells (<xref ref-type="bibr" rid="B31">Malladi et&#x20;al., 2016</xref>). Based on these findings, it is necessary to evaluate the role of <italic>DKK1</italic> in the cancer immune microenvironment.</p>
<p>For several years, numerous studies have been conducted to explore the role of <italic>DKK1</italic> in various cancers and revealed the different roles of <italic>DKK1</italic> in different cancer types. In this study, we evaluated the pan-cancer expression of <italic>DKK1</italic> using The Cancer Genome Atlas (TCGA) dataset. Subsequently, we investigated the association of <italic>DKK1</italic> expression with the survival time of patients with different cancers. Finally, we analyzed the effect of <italic>DKK1</italic> expression on immune cell infiltration and immune cell markers. The overall process of this research is shown in <xref ref-type="fig" rid="F1">Figure&#x20;1</xref>. Our findings deepened our understanding of the roles of <italic>DKK1</italic> in cancer progression and prognosis.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>The overall flow chart of <italic>DKK1</italic> in pan-cancer analysis.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g001.tif"/>
</fig>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and Methods</title>
<sec id="s2-1">
<title>Pan-Cancer <italic>DKK1</italic> Expression Profile Analysis</title>
<p>The Genotype-Tissue Expression (GTEx) project and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were downloaded from UCSC Xena (<ext-link ext-link-type="uri" xlink:href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</ext-link>) and used for pan-cancer analysis of <italic>DKK1</italic>. TOIL was used to reprocess the raw RNA-seq data from GTEx and TCGA databases to correct batch effects and allow for data merging across GTEx and TCGA datasets (<xref ref-type="bibr" rid="B46">Vivian et&#x20;al., 2017</xref>). Expression differences of <italic>DKK1</italic> were examined using the Wilcoxon rank test with the threshold of &#x7c;log<sub>2</sub> FC&#x7c; &#x3e;1 and <italic>p</italic>-value &#x3c;&#x20;0.05.</p>
</sec>
<sec id="s2-2">
<title>Survival Analysis</title>
<p>Patients with different types of cancer were segregated into high and low expression groups by the median of the expression level of <italic>DKK1</italic>. Kaplan-Meier (KM) survival analysis was conducted by R <italic>survival</italic> and <italic>survMiner</italic> packages. Cox regression analysis was used to evaluate the relationship between <italic>DKK1</italic> expression and overall survival (OS) based on TCGA data. Univariate Cox analysis was performed to select relevant variables, and a multivariate Cox model was used to evaluate the independent prognostic factors. Differences were considered significant when <italic>p</italic>-values were less than 0.05. All analyses were carried out using R language (version&#x20;3.6.3).</p>
</sec>
<sec id="s2-3">
<title>Correlations Between <italic>DKK1</italic> Expression and Infiltration Immune Cells</title>
<p>Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration signatures of each individual with LUAD according to the expression level of <italic>DKK1</italic> by using the R <italic>GSVA</italic> package (<xref ref-type="bibr" rid="B17">H&#xe4;nzelmann et&#x20;al., 2013</xref>). The gene set for immune cell markers was retrieved from the Laboratory of Integrative Cancer Immunology (LICI) (<xref ref-type="bibr" rid="B8">Bindea et&#x20;al., 2013</xref>).</p>
</sec>
<sec id="s2-4">
<title>Correlation Between <italic>DKK1</italic> Expression and Immune Cell Markers</title>
<p>The correlation module in TIMER <underline>(</underline>
<ext-link ext-link-type="uri" xlink:href="http://timer.cistrome.org/">http://timer.cistrome.org/</ext-link>) was used to analyze the correlation between the expression of <italic>DKK1</italic> and diverse immune cell markers. The list of different immune factors was obtained from the tumor immune system interaction database (<xref ref-type="bibr" rid="B37">Ru et&#x20;al., 2019</xref>).</p>
</sec>
<sec id="s2-5">
<title>PPI Network Construction and Functional Enrichment Analysis</title>
<p>The protein-protein interaction network (PPI network) of <italic>DKK1</italic> was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, <ext-link ext-link-type="uri" xlink:href="http://string-db.org/">http://string-db.org</ext-link>) (<xref ref-type="bibr" rid="B32">von Mering et&#x20;al., 2003</xref>) with a minimum required interaction score &#x3e; 0.7. To identify the hub genes of <italic>DKK1</italic> PPI, the maximal clique centrality (MCC) algorithm was analyzed by the Cytohubba (<xref ref-type="bibr" rid="B9">Chin et&#x20;al., 2014</xref>) plugin based on Cytoscape (<xref ref-type="bibr" rid="B39">Shannon et&#x20;al., 2003</xref>). To evaluate the biological functions that <italic>DKK1</italic> is involved in, Gene Ontology (GO, <ext-link ext-link-type="uri" xlink:href="http://geneontology.org/">http://geneontology.org/</ext-link>) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG, <ext-link ext-link-type="uri" xlink:href="http://www.kegg.jp/">http://www.kegg.jp/</ext-link>) pathway analyses were performed using the R package clusterProfiler (<xref ref-type="bibr" rid="B51">Yu et&#x20;al., 2012</xref>).</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Results</title>
<sec id="s3-1">
<title>
<italic>DKK1</italic> Expression Difference in Pan-Cancer</title>
<p>In this study, expression difference analyses of <italic>DKK1</italic> were performed between cancer tissues and adjacent normal tissues. <italic>DKK1</italic> mRNA expression in cancer tissues from the TCGA database was inconsistent with that in GTEx and TCGA normal tissues (<xref ref-type="fig" rid="F2">Figures 2A,B</xref>). <italic>DKK1</italic> expression was significantly higher in cancer tissues with cholangiocarcinoma (CHOL), ESCA, HNSC, LIHC, lung squamous cell carcinoma (LUSC), and STAD than that in their respective adjacent normal tissues. However, <italic>DKK1</italic> expression was significantly decreased in bladder urothelial carcinoma (BLCA), kidney chromophobe (KICH), kidney renal papillary cell carcinoma (KIRP), and prostate adenocarcinoma (PRAD).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Pan-cancer analysis of differential expression of <italic>DKK1</italic>. The abundance is measured by log-normalized transcripts per million (TPM). <bold>(A)</bold> Differential expression analysis between unpaired cancer tissues and adjacent normal tissues. <bold>(B)</bold>. Differential expression analysis between paired cancer tissues and adjacent normal tissues.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g002.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>
<italic>DKK1</italic> Expression Correlates With Cancer Prognosis and Clinical Stages</title>
<p>To analyze the association of <italic>DKK1</italic> expression with clinical outcomes across all TCGA cancer types. TCGA pan-cancer analyses showed that higher <italic>DKK1</italic> level was significantly associated with the poor prognosis of adrenocortical carcinoma (ACC) (<italic>p</italic>&#x20;&#x3c; 0.01), HNSC (<italic>p</italic>&#x20;&#x3c; 0.01), LAML (<italic>p</italic>&#x20;&#x3d; 0.046), lung adenocarcinoma (LUAD) (<italic>p</italic>&#x20;&#x3c; 0.01), mesothelioma (MESO) (<italic>p</italic>&#x20;&#x3c; 0.01), PAAD (<italic>p</italic>&#x20;&#x3c; 0.01), and STAD (<italic>p</italic>&#x20;&#x3c; 0.01) (<xref ref-type="fig" rid="F3">Figures 3A&#x2013;G</xref>), and lower <italic>DKK1</italic> expression was significantly associated with the poor prognosis of ESCA (<italic>p</italic>&#x20;&#x3d; 0.016) and kidney renal clear cell carcinoma (KIRC) (<italic>p</italic>&#x20;&#x3d; 0.024) (<xref ref-type="fig" rid="F3">Figures 3H,I</xref>). In addition, the expression of <italic>DKK1</italic> was closely related to the clinical stages of several cancer types, including ACC, KIRC, and PAAD (<xref ref-type="fig" rid="F4">Figures 4A&#x2013;C</xref>). These results indicated that <italic>DKK1</italic> was a potential oncogene in many types of cancer.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Kaplan-Meier analysis of <italic>DKK1</italic> expression in different cancer types. <bold>(A&#x2013;G)</bold> Higher <italic>DKK1</italic> expression was correlated with the poor prognosis of ACC, HNSC, LAML, LUAD, MESO, PAAD, and STAD. <bold>(H&#x2013;I)</bold> Lower <italic>DKK1</italic> expression was correlated with the poor prognosis of ESCA and KIRC.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g003.tif"/>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Association of <italic>DKK1</italic> expression with tumor stages. <bold>(A&#x2013;C)</bold> <italic>DKK1</italic> expression in different stages of ACC, KIRC, and PAAD. Data shown as mean&#x20;&#xb1; SD. &#x2a;<italic>p</italic>&#x20;&#x3c; 0.05, &#x2a;&#x2a;<italic>p</italic>&#x20;&#x3c; 0.01, &#x2a;&#x2a;&#x2a;<italic>p</italic>&#x20;&#x3c; 0.001.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g004.tif"/>
</fig>
</sec>
<sec id="s3-3">
<title>DKK1 Expression as an Independent Prognostic Factor</title>
<p>Differential <italic>DKK1</italic> expression was associated with poor overall survival (OS) in several types of cancer (<italic>p</italic>&#x20;&#x3c; 0.05; <xref ref-type="fig" rid="F3">Figure&#x20;3</xref>). To explore possible correlations of <italic>DKK1</italic> expression with clinical factors, several potential survival-related variables, including TNM stages, gender, age, smoking status, and <italic>DKK1</italic> expression, were entered into a multivariate Cox model. The results suggested that <italic>DKK1</italic> expression level was an independent protective factor for prognosis of ESCA patients [hazard ratio (HR) &#x3d; 0.53, 95% confidence interval (CI) &#x3d; 0.28&#x2013;0.99, <italic>p</italic>&#x20;&#x3c; 0.05; <xref ref-type="table" rid="T1">Table&#x20;1</xref>), LUAD (HR &#x3d; 1.95, 95% CI &#x3d;&#x20;1.40&#x2013;2.72, <italic>p</italic>&#x20;&#x3c; 0.01; <xref ref-type="table" rid="T2">Table&#x20;2</xref>), MESO (HR &#x3d; 2.07, 95% CI &#x3d;&#x20;1.29&#x2013;3.33, <italic>p</italic>&#x20;&#x3c; 0.01; <xref ref-type="table" rid="T3">Table&#x20;3</xref>), and STAD (HR &#x3d; 1.70, 95% CI &#x3d; 1.19&#x2013;2.44, <italic>p</italic>&#x20;&#x3c; 0.01; <xref ref-type="table" rid="T4">Table&#x20;4</xref>). Taken together, these results demonstrated that differential <italic>DKK1</italic> expression had a non-directional effect on the progression and prognosis of certain cancers.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Univariate and multivariate Cox analyses of <italic>DKK1</italic> expression with overall survival (OS) among esophageal carcinoma (ESCA) patients.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="left">Characteristics</th>
<th rowspan="2" align="center">Total (N)</th>
<th colspan="2" align="center">Univariate analysis</th>
<th colspan="2" align="center">Multivariate analysis</th>
</tr>
<tr>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">T stage (T3 &#x26; T4 vs. T1 &#x26; T2)</td>
<td align="char" char=".">145</td>
<td align="char" char="(">1.312 (0.756&#x2013;2.277)</td>
<td align="char" char=".">0.334</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">N stage (N1 &#x26; N2 &#x26; N3 vs. N0)</td>
<td align="char" char=".">144</td>
<td align="char" char="(">2.970 (1.606&#x2013;5.493)</td>
<td align="char" char=".">&#x3c;0.001</td>
<td align="char" char="(">2.483 (1.221&#x2013;5.049)</td>
<td align="char" char=".">0.012</td>
</tr>
<tr>
<td align="left">M stage (M1 vs. M0)</td>
<td align="char" char=".">129</td>
<td align="char" char="(">5.075 (2.312&#x2013;11.136)</td>
<td align="char" char=".">&#x3c;0.001</td>
<td align="char" char="(">3.378 (1.523&#x2013;7.495)</td>
<td align="char" char=".">0.003</td>
</tr>
<tr>
<td align="left">Gender (male vs. female)</td>
<td align="char" char=".">162</td>
<td align="char" char="(">2.306 (0.922&#x2013;5.770)</td>
<td align="char" char=".">0.074</td>
<td align="char" char="(">1.878 (0.557&#x2013;6.332)</td>
<td align="char" char=".">0.309</td>
</tr>
<tr>
<td align="left">Age (&#x3e;60 vs. &#x3c;&#x3d;60)</td>
<td align="char" char=".">162</td>
<td align="char" char="(">0.831 (0.506&#x2013;1.365)</td>
<td align="char" char=".">0.466</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Smoker (yes vs. no)</td>
<td align="char" char=".">144</td>
<td align="char" char="(">1.539 (0.799&#x2013;2.966)</td>
<td align="char" char=".">0.197</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">DKK1 (high vs. low)</td>
<td align="char" char=".">162</td>
<td align="char" char="(">0.529 (0.315&#x2013;0.888)</td>
<td align="char" char=".">0.016</td>
<td align="char" char="(">0.530 (0.283&#x2013;0.991)</td>
<td align="char" char=".">0.047</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Univariate and multivariate Cox analyses of <italic>DKK1</italic> expression with overall survival (OS) among lung adenocarcinoma (LUAD) patients.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="left">Characteristics</th>
<th rowspan="2" align="center">Total (N)</th>
<th colspan="2" align="center">Univariate analysis</th>
<th colspan="2" align="center">Multivariate analysis</th>
</tr>
<tr>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">T stage (T2 &#x26; T3 &#x26; T4 vs. T1)</td>
<td align="char" char=".">523</td>
<td align="char" char="(">1.728 (1.229&#x2013;2.431)</td>
<td align="char" char=".">0.002</td>
<td align="char" char="(">1.739 (1.117&#x2013;2.709)</td>
<td align="char" char=".">0.014</td>
</tr>
<tr>
<td align="left">N stage (N1 &#x26; N2 &#x26; N3 vs. N0)</td>
<td align="char" char=".">510</td>
<td align="char" char="(">2.601 (1.944&#x2013;3.480)</td>
<td align="char" char=".">&#x3c;0.001</td>
<td align="char" char="(">2.524 (1.809&#x2013;3.521)</td>
<td align="char" char=".">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">M stage (M1 vs. M0)</td>
<td align="char" char=".">377</td>
<td align="char" char="(">2.136 (1.248&#x2013;3.653)</td>
<td align="char" char=".">0.006</td>
<td align="char" char="(">1.868 (1.047&#x2013;3.332)</td>
<td align="char" char=".">0.034</td>
</tr>
<tr>
<td align="left">Gender (male vs. female)</td>
<td align="char" char=".">526</td>
<td align="char" char="(">1.070 (0.803&#x2013;1.426)</td>
<td align="char" char=".">0.642</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Age (&#x3e;65 vs. &#x3c;&#x3d;65)</td>
<td align="char" char=".">516</td>
<td align="char" char="(">1.223 (0.916&#x2013;1.635)</td>
<td align="char" char=".">0.172</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Smoker (yes vs. no)</td>
<td align="char" char=".">512</td>
<td align="char" char="(">0.894 (0.592&#x2013;1.348)</td>
<td align="char" char=".">0.591</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">DKK1 (high vs. low)</td>
<td align="char" char=".">526</td>
<td align="char" char="(">2.022 (1.505&#x2013;2.717)</td>
<td align="char" char=".">&#x3c;0.001</td>
<td align="char" char="(">1.949 (1.397&#x2013;2.718)</td>
<td align="char" char=".">&#x3c;0.001</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Univariate and multivariate Cox analyses of <italic>DKK1</italic> expression with overall survival (OS) among mesothelioma (MESO) patients.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="left">Characteristics</th>
<th rowspan="2" align="center">Total (N)</th>
<th colspan="2" align="center">Univariate analysis</th>
<th colspan="2" align="center">Multivariate analysis</th>
</tr>
<tr>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">T stage (T3 &#x26; T4 vs. T1 &#x26; T2)</td>
<td align="char" char=".">83</td>
<td align="char" char="(">0.955 (0.590&#x2013;1.547)</td>
<td align="char" char=".">0.852</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">N stage (N1 &#x26; N2 &#x26; N3 vs. N0)</td>
<td align="char" char=".">81</td>
<td align="char" char="(">0.904 (0.557&#x2013;1.467)</td>
<td align="char" char=".">0.683</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">M stage (M1 vs. M0)</td>
<td align="char" char=".">59</td>
<td align="char" char="(">1.917 (0.454&#x2013;8.089)</td>
<td align="char" char=".">0.376</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Gender (male vs. female)</td>
<td align="char" char=".">85</td>
<td align="char" char="(">0.944 (0.516&#x2013;1.726)</td>
<td align="char" char=".">0.850</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Age (&#x3e;65 vs. &#x3c;&#x3d;65)</td>
<td align="char" char=".">85</td>
<td align="char" char="(">1.296 (0.805&#x2013;2.085)</td>
<td align="char" char=".">0.286</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">DKK1 (high vs. low)</td>
<td align="char" char=".">85</td>
<td align="char" char="(">2.069 (1.285&#x2013;3.332)</td>
<td align="char" char=".">0.003</td>
<td align="char" char="(">2.069 (1.285&#x2013;3.332)</td>
<td align="char" char=".">0.003</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Univariate and multivariate Cox analyses of <italic>DKK1</italic> expression with overall survival (OS) among stomach adenocarcinoma (STAD) patients.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="left">Characteristics</th>
<th rowspan="2" align="center">Total (N)</th>
<th colspan="2" align="center">Univariate analysis</th>
<th colspan="2" align="center">Multivariate analysis</th>
</tr>
<tr>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
<th align="center">Hazard ratio (95% CI)</th>
<th align="center">
<italic>p</italic> Value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">T stage (T3 &#x26; T4 vs. T1 &#x26; T2)</td>
<td align="char" char=".">362</td>
<td align="char" char="(">1.719 (1.131&#x2013;2.612)</td>
<td align="char" char=".">0.011</td>
<td align="char" char="(">1.461 (0.922&#x2013;2.316)</td>
<td align="char" char=".">0.106</td>
</tr>
<tr>
<td align="left">N stage (N1 &#x26; N2 &#x26; N3 vs. N0)</td>
<td align="char" char=".">352</td>
<td align="char" char="(">1.925 (1.264&#x2013;2.931)</td>
<td align="char" char=".">0.002</td>
<td align="char" char="(">1.589 (1.007&#x2013;2.507)</td>
<td align="char" char=".">0.047</td>
</tr>
<tr>
<td align="left">M stage (M1 vs. M0)</td>
<td align="char" char=".">352</td>
<td align="char" char="(">2.254 (1.295&#x2013;3.924)</td>
<td align="char" char=".">0.004</td>
<td align="char" char="(">2.630 (1.459&#x2013;4.743)</td>
<td align="char" char=".">0.001</td>
</tr>
<tr>
<td align="left">Gender (male vs. female)</td>
<td align="char" char=".">370</td>
<td align="char" char="(">1.267 (0.891&#x2013;1.804)</td>
<td align="char" char=".">0.188</td>
<td align="left"/>
<td align="left"/>
</tr>
<tr>
<td align="left">Age (&#x3e;65 vs. &#x3c;&#x3d;65)</td>
<td align="char" char=".">367</td>
<td align="char" char="(">1.620 (1.154&#x2013;2.276)</td>
<td align="char" char=".">0.005</td>
<td align="char" char="(">1.953 (1.355&#x2013;2.816)</td>
<td align="char" char=".">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">DKK1 (high vs. low)</td>
<td align="char" char=".">370</td>
<td align="char" char="(">1.554 (1.114&#x2013;2.167)</td>
<td align="char" char=".">0.009</td>
<td align="char" char="(">1.704 (1.192&#x2013;2.436)</td>
<td align="char" char=".">0.003</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-4">
<title>Association Between <italic>DKK1</italic> Expression and Immune Responses in Cancer</title>
<p>To further validate the role of <italic>DKK1</italic> as a potential immune influencer, the relationship between <italic>DKK1</italic> expression and immune cell infiltration was estimated. It turned out that <italic>DKK1</italic> was strongly correlated with the immune cell infiltration in many types of cancer (&#x7c;<italic>r</italic>&#x7c; &#x3e; 0.4, <italic>p</italic>&#x20;&#x3c; 0.05). The infiltration level of Th2 cells was positively correlated with the expression of <italic>DKK1</italic> in ACC, KICH, MESO, and PAAD (<xref ref-type="fig" rid="F5">Figures 5A&#x2013;D</xref>). <italic>DKK1</italic> level was also correlated with immune cell infiltration of macrophages (GBM), neutrophils (PRAD), Th1 cells (SARC), NK cells (TGCT), Th1 cells (THYM), and Tgd (UVM) (<xref ref-type="sec" rid="s10">Supplementary Figures S1A&#x2013;F</xref>).</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Effects of <italic>DKK1</italic> expression on immune cell infiltration status. <bold>(A)</bold> ACC; <bold>(B)</bold> KICH; <bold>(C)</bold> MESO; and <bold>(D)</bold> PAAD.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g005.tif"/>
</fig>
<p>Using the data from the TIMER database, we evaluated the correlation between <italic>DKK1</italic> and immune infiltrating cells. Recognizable immune cell markers included B&#x20;cells, T&#x20;cells (general), CD8<sup>&#x2b;</sup> T&#x20;cells, T&#x20;cells with different functions, M1 and M2 macrophages, TAMs, monocytes, NK cells, neutrophils, and dendritic cells. More directly, our findings provided evidence that the expression of <italic>DKK1</italic> was correlated with the level of Th1 markers (<italic>STAT1</italic> and <italic>IFNG</italic>) and Th2 markers (<italic>GATA3</italic> and <italic>STAT6</italic>) in various cancers, such as ACC, KICH, MESO, and PAAD (<xref ref-type="fig" rid="F6">Figure&#x20;6</xref>; <xref ref-type="sec" rid="s10">Supplementary Table S1</xref>). In addition, <italic>DKK1</italic> was also correlated with the level of other immune cell markers, such as GBM (macrophage markers), PRAD (neutrophils markers), and TGCT (NK cell markers) (<xref ref-type="sec" rid="s10">Supplementary Table S2</xref>). These results suggested that <italic>DKK1</italic> might have a large impact on the tumor immune microenvironment.</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>The integrated network of <italic>DKK1</italic>. <bold>(A)</bold> Protein-protein interaction network of <italic>DKK1</italic>; <bold>(B)</bold> gene-concept network for KEGG pathway analysis; and <bold>(C)</bold> gene-concept network for GO terms analysis.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g006.tif"/>
</fig>
</sec>
<sec id="s3-5">
<title>Protein&#x2013;Protein Interaction Network, Gene Ontology, and KEGG Pathway Analyses</title>
<p>A PPI network of DKK1 included 46 nodes and 506 edges. The hub genes were screened out using the Cytoscape app cytoHubba plugin. The top five hub genes were <italic>DKK1</italic>, <italic>WNT1</italic>, <italic>WNT</italic>2, <italic>WNT3A,</italic> and <italic>WNT5A</italic> (<xref ref-type="fig" rid="F7">Figure&#x20;7A</xref>), which indicated that <italic>DKK1</italic> might be involved in the functional regulation of WNT family genes. To further clarify the biological functions of <italic>DKK1</italic>, KEGG pathway and GO terms analyses were performed. KEGG pathway analysis showed that genes in the <italic>DKK1</italic> PPI were mainly enriched in the WNT signaling pathway, basal cell carcinoma, breast cancer, gastric cancer, and hepatocellular carcinoma pathways (<xref ref-type="fig" rid="F7">Figure&#x20;7B</xref>). GO terms were mainly concentrated in regulation of the WNT signaling pathway (biological process, BP), WNT signalosome (cellular component, CC), and frizzled binding (molecular function, MF) (<xref ref-type="fig" rid="F7">Figure&#x20;7C</xref>). These results implied that genes in the <italic>DKK1</italic> PPI might work together to participate in cancer progression by the WNT signaling pathway.</p>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>
<italic>DKK1</italic> expression is related to the changes in Th1 and Th2 cells of ACC, LICH, MESO, and PAAD. The markers include <italic>STAT1</italic> and <italic>IFNG</italic> of Th1 cells; <italic>GATA3</italic> and <italic>STAT6</italic> of Th2. Scatter plot of <italic>DKK1</italic> expression in ACC <bold>(A)</bold> (<italic>n</italic>&#x20;&#x3d; 79), KICH <bold>(B)</bold> (<italic>n</italic>&#x20;&#x3d; 66), MESO <bold>(C)</bold> (<italic>n</italic>&#x20;&#x3d; 87), and PAAD <bold>(D)</bold> (<italic>n</italic>&#x20;&#x3d; 179) correlated with Th1 and Th2 cell gene markers.</p>
</caption>
<graphic xlink:href="fgene-12-757897-g007.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>
<italic>DKK1</italic> is a well-established crucial participant in the WNT signal pathway and also acts as a major target in drug design. DKN-01, an anti-DKK1 mAb, could block the immunosuppressive effects of <italic>DKK1</italic> in the tumor microenvironment (TME) (<xref ref-type="bibr" rid="B15">Haas et&#x20;al., 2021</xref>) and also perform potential antiangiogenic and immunomodulatory activity in combination therapy with gemcitabine/cisplatin in advanced biliary tract cancer (<xref ref-type="bibr" rid="B14">Goyal et&#x20;al., 2020</xref>). Other clinical trials using anti-DKK1 mAb, such as BHQ880 (<xref ref-type="bibr" rid="B12">Fulciniti et&#x20;al., 2009</xref>) and PF-04840082 (<xref ref-type="bibr" rid="B6">Betts et&#x20;al., 2010</xref>) were also carried out to treat certain cancers.</p>
<p>The differential expression of <italic>DKK1</italic> has been reported in many different cancers; however, there is a lack of comprehensive pan-cancer analysis of <italic>DKK1</italic>. In the current study, after analyzing the expression level of <italic>DKK1</italic> in cancer and normal tissues of 33 cancer types, we found that <italic>DKK1</italic> was upregulated in CHOL, ESCA, HNSC, LIHC, LUSC, and STAD, but downregulated in BLCA, KICH, KIRP, and PRAD. Similarly, several studies demonstrated that <italic>DKK1</italic> was differentially expressed in a variety of cancers and affected cancer progression by changing cancer proliferation and invasion capabilities (<xref ref-type="bibr" rid="B40">Shi et&#x20;al., 2013</xref>; <xref ref-type="bibr" rid="B53">Zhuang et&#x20;al., 2017</xref>; <xref ref-type="bibr" rid="B11">Fezza et&#x20;al., 2019</xref>). Our prognostic analysis showed that in most cancers (ACC, HNSC, LAML, LUAD, MESO, PAAD, and STAD), the upregulated expression of <italic>DKK1</italic> was associated with poor prognosis. This further supported the results from previous studies which also confirmed the relationship between the overexpression of <italic>DKK1</italic> and the lower overall survival in HNSC (<xref ref-type="bibr" rid="B13">Gao et&#x20;al., 2018</xref>), NSCLC (<xref ref-type="bibr" rid="B49">Yamabuki et&#x20;al., 2007</xref>), and PAAD (<xref ref-type="bibr" rid="B16">Han et&#x20;al., 2015</xref>). However, in ESCA and KIRC, our data showed that a lower <italic>DKK1</italic> level contributed to poor prognosis. Other pan-cancer analysis also provided evidence that the same gene could result in a controversial consequence in different kinds of cancer. For example, a pan-cancer study showed that there was a significant different expression of <italic>NLRP3</italic> in 15 different cancers, and it was used as an independent posterior factor of SKCM (<xref ref-type="bibr" rid="B21">Ju et&#x20;al., 2021</xref>). Similarly, when comparing the expression level of <italic>Fam20C</italic> in cancer tissues with that in neighboring normal tissues, we found a large variation across different kinds of cancers which indicated the different roles in different cancers (<xref ref-type="bibr" rid="B28">Liu et&#x20;al., 2021</xref>).</p>
<p>The effect of <italic>DKK1</italic> on various cancers may be the result of abnormal activation of WNT signaling (<xref ref-type="bibr" rid="B33">Niida et&#x20;al., 2004</xref>). Our PPI analysis and the pathway enrichment analysis showed that the genes interacting with <italic>DKK1</italic> were mainly involved in the WNT signaling pathway, basal cell carcinoma, breast cancer, gastric cancer, and liver cancer. <italic>DKK</italic>1 has been reported to inhibit the interaction of <italic>LRP 5/6</italic> with a <italic>WNT</italic> signal and the formation of the Fzd-WNT-LRP5/6 complex (<xref ref-type="bibr" rid="B48">Wirths et&#x20;al., 2003</xref>; <xref ref-type="bibr" rid="B2">Ahn et&#x20;al., 2011</xref>). By interfering with <italic>DKK1</italic>, the activated WNT3a/&#x3b2;-catenin signal had a great influence on cell proliferation, cell cycle acceleration, invasion, and migration (<xref ref-type="bibr" rid="B36">Ren et&#x20;al., 2021</xref>). These results suggested that <italic>DKK1</italic> may participate in cancer progression through the WNT signaling pathway.</p>
<p>To unravel the potential mechanism of the predictive value of DKK1 alterations for the tumor immune microenvironment, types of infiltrating immune cells were surveyed. Chronic inflammation is a well-acknowledged risk factor of cancers, we hypothesized that DKK1 influenced cancer prognosis through immune cell infiltration. After conducting immune infiltration analysis, we found that the expression <italic>DKK1</italic> was correlated with certain immune cell markers on Th1 and Th2 cells. Wang&#x2019;s study showed that Th2 cells were identified as prognostic immune cells in gastric cancer (<xref ref-type="bibr" rid="B47">Wang et&#x20;al., 2020</xref>). Th2 cells also demonstrated therapeutic potential for adoptive cell therapy (ACT) (<xref ref-type="bibr" rid="B29">Lorvik et&#x20;al., 2016</xref>). Recent studies demonstrated that Th2 responses played a critical role in the pathogenesis of cancers, such as luminal breast cancer (<xref ref-type="bibr" rid="B52">Zhang et&#x20;al., 2015</xref>), prostate and advanced melanoma cancer (<xref ref-type="bibr" rid="B10">Dulos et&#x20;al., 2012</xref>), and myeloma (<xref ref-type="bibr" rid="B45">Tian et&#x20;al., 2019</xref>). The deep understanding of the relationship between DKK1 and Th2 responses will help us comprehend the mechanism of local antitumor response. In recent years, with the development of immune checkpoint inhibitors, infiltrating immune cell markers can not only be used as prognostic markers, but also have received extensive attention as a new type of treatment (<xref ref-type="bibr" rid="B25">Ladanyi, 2015</xref>).</p>
<p>Taken together, our study annotated <italic>DKK1</italic> expression in a pan-cancer manner and identified that <italic>DKK1</italic> could be used as an independent prognosis factor. <italic>DKK1</italic> was significantly expressed in various cancers, and it might also be a biomarker for tumor immunity or even targeted therapy. This study also provided evidence of the effect of <italic>DKK1</italic> on immune cell infiltration. However, this study has its limitations. Since all analyses were based on online datasets, experimental confirmation from a laboratory is still needed.</p>
</sec>
</body>
<back>
<sec id="s5">
<title>Data Availability Statement</title>
<p>Publicly available datasets were analyzed in this study. This data can be found here: <ext-link ext-link-type="uri" xlink:href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</ext-link>
</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>HZ conceived and designed the research. SG analyzed the data. SG and YJ wrote the original draft. HZ edited and formed the final version. All authors have read and approved the final version of the article.</p>
</sec>
<sec id="s6">
<title>Funding</title>
<p>This research was funded by the Basic Scientific Research Funds for Provincial Universities of North China University of Science and Technology (JQN2019013) and Postgraduate Innovation Funding Project of Hebei Province (CXZZBS2021105).</p>
</sec>
<sec sec-type="COI-statement" id="s8">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s9">
<title>Publisher&#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="s10">
<title>Supplementary Material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fgene.2021.757897/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fgene.2021.757897/full&#x23;supplementary-material</ext-link>
</p>
<supplementary-material xlink:href="Table2.xlsx" id="SM1" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="Table1.xlsx" id="SM2" mimetype="application/xlsx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
<supplementary-material xlink:href="DataSheet1.docx" id="SM3" mimetype="application/docx" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<ref-list>
<title>References</title>
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