{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T04:01:34Z","timestamp":1780200094695,"version":"3.54.0"},"reference-count":17,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"vor","delay-in-days":149,"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":["International Journal of Genomics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy with complex genetic underpinnings. Understanding the causal relationships between genetic factors and AML risk is crucial for developing targeted therapeutic strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a comprehensive Mendelian randomization (MR) analysis to evaluate causal effects of 10 genetic exposures on AML risk using multiple analytical methods including inverse\u2010variance weighted, weighted median, simple mode, weighted mode, and MR\u2010Egger regression. Single\u2010cell RNA sequencing analysis was performed to validate gene expression patterns and investigate cellular heterogeneity in AML. Quality control, clustering analysis, and cell type annotation were conducted to characterize the expression profiles of identified risk genes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      MR analysis revealed heterogeneous causal effects across genetic exposures. Three genes demonstrated significant protective effects:\n                      <jats:italic>COL11A2<\/jats:italic>\n                      (OR: 0.425\u20130.481),\n                      <jats:italic>MTHFD1<\/jats:italic>\n                      (OR: 0.142\u20130.151), and\n                      <jats:italic>SERPINA10<\/jats:italic>\n                      (OR: 0.426\u20130.560). Seven genes showed risk\u2010increasing effects:\n                      <jats:italic>SPATA20<\/jats:italic>\n                      ,\n                      <jats:italic>PDE5A<\/jats:italic>\n                      ,\n                      <jats:italic>ANXA11<\/jats:italic>\n                      ,\n                      <jats:italic>FUT10<\/jats:italic>\n                      ,\n                      <jats:italic>TXNL4B<\/jats:italic>\n                      ,\n                      <jats:italic>RNASET2<\/jats:italic>\n                      , and\n                      <jats:italic>TCL1A<\/jats:italic>\n                      , with\n                      <jats:italic>PDE5A<\/jats:italic>\n                      showing the strongest risk association (OR &gt; 8.0). Single\u2010cell analysis identified 17 distinct cell populations and 14 cell types, revealing cell\u2010specific expression patterns of these risk genes across different hematopoietic lineages.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This integrative approach provides robust evidence for causal relationships between specific genetic factors and AML risk, offering insights into disease mechanisms and potential therapeutic targets at the cellular level.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/ijog\/5584620","type":"journal-article","created":{"date-parts":[[2026,5,31]],"date-time":"2026-05-31T03:30:52Z","timestamp":1780198252000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Analysis of Genetic Risk Factors for Acute Myeloid Leukemia Using Mendelian Randomization and Single\u2010Cell RNA Sequencing Validation"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9821-2021","authenticated-orcid":false,"given":"Tian","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7427-4029","authenticated-orcid":false,"given":"Ruiting","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7614-446X","authenticated-orcid":false,"given":"Guocai","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4911-130X","authenticated-orcid":false,"given":"Yunguang","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3763-5365","authenticated-orcid":false,"given":"Dasi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.blre.2019.04.005"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.22233"},{"key":"e_1_2_13_3_2","doi-asserted-by":"crossref","DOI":"10.1038\/s41408-021-00425-3","article-title":"Acute Myeloid Leukemia: Current Progress and Future Directions","volume":"11","author":"Kantarjian H.","year":"2021","journal-title":"Blood Cancer Journal"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/0962280215597579"},{"key":"e_1_2_13_5_2","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1093\/ije\/dyr036","article-title":"Avoiding Bias from Weak Instruments in Mendelian Randomization Studies","volume":"40","author":"Burgess S.","year":"2011","journal-title":"International Journal of Epidemiology"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2019.01.031"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1365330"},{"key":"e_1_2_13_8_2","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1038\/s41375-022-01789-6","article-title":"Single-Cell Analysis Reveals the Chemotherapy-Induced Cellular Reprogramming and Novel Therapeutic Targets in Relapsed\/Refractory Acute Myeloid Leukemia","volume":"37","author":"Li K.","year":"2023","journal-title":"Leukemia"},{"key":"e_1_2_13_9_2","article-title":"A Single-Cell Survey of Cellular Hierarchy in Acute Myeloid Leukemia","volume":"13","author":"Wu J.","year":"2020","journal-title":"Journal of Hematology & Oncology"},{"key":"e_1_2_13_10_2","doi-asserted-by":"crossref","DOI":"10.1186\/s13045-023-01424-6","article-title":"Recent Advances in Targeted Therapies in Acute Myeloid Leukemia","volume":"16","author":"Bhansali R. 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