{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T16:09:49Z","timestamp":1773677389366,"version":"3.50.1"},"reference-count":54,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":74,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Mediators of Inflammation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Mendelian randomization (MR) analysis has currently been widely used in combination with expression quantitative trait loci (eQTLs) or cis\u2010eQTLs for identifying novel drug targets in various diseases. Hence in this article, we first integrated MR analysis with eQTLs and cis\u2010eQTLs to reveal the causal effects among SLC2A9 and renal cancer (RC), along with its related metabolic mechanisms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Related genome\u2010wide association study (GWAS) data were obtained from online datasets. Two\u2010sample MR and summary\u2010data\u2010based MR study (SMR) analyses were applied to assess the causal effects among SLC2A9 eQTL or cis\u2010eQTLs and RC, respectively. Sensitivity analysis confirmed the robustness and heterogeneity of the results. Besides, 1400 metabolites were enrolled as mediators to further reveal the related metabolic mechanisms of SLC2A9 eQTL involved in RC.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our results found that SLC2A9 eQTL was markedly associated with low risks of RC in the IEU OpenGWAS eQTLs dataset (discovering) (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.030). The eQTLGEN and GTEx Whole Blood datasets validated the causal effects among SLC2A9 cis\u2010eQTL and RC, reducing RC risks (both\n                      <jats:italic>p<\/jats:italic>\n                      \u2009 &lt; 0.05). The TCGA\u2010KIRC and the CPTAC\u2010KIRC datasets validated the mRNA and protein expression levels of SLC2A9 in RC, confirming its antitumor roles in RC (both\n                      <jats:italic>p<\/jats:italic>\n                      \u2009 &lt; 0.05). A total of five metabolites were finally identified to reveal the related metabolic mechanisms of SLC2A9 eQTL involved in RC (all\n                      <jats:italic>p<\/jats:italic>\n                      \u2009 &lt; 0.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study indicated that SLC2A9 eQTL or cis\u2010eQTL was markedly associated with low risks of RC and played an antitumor role in RC, along with preliminary identification of five metabolic pathways for its potential mechanisms requiring further investigation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/mi\/5817314","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T14:36:01Z","timestamp":1773671761000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mendelian Randomization Identified SLC2A9 as a Novel cis\u2010eQTL\u2010Mediated Susceptibility Gene in Suppressing Renal Cancer and Its Related Metabolic Mechanisms"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2067-5465","authenticated-orcid":false,"given":"Yi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4741-1057","authenticated-orcid":false,"given":"Yu","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5282-5600","authenticated-orcid":false,"given":"Hao","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6290-4863","authenticated-orcid":false,"given":"Bingye","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4485-0253","authenticated-orcid":false,"given":"Huyang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,3,16]]},"reference":[{"key":"e_1_2_15_1_2","first-page":"12","article-title":"Cancer Statistics","volume":"74","author":"Siegel R. 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