{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T08:09:26Z","timestamp":1778659766774,"version":"3.51.4"},"reference-count":42,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:00:00Z","timestamp":1778630400000},"content-version":"vor","delay-in-days":132,"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"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ50220"],"award-info":[{"award-number":["2023JJ50220"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Mutation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acute myocardial infarction (AMI) involves complex immune responses and cellular interaction mechanisms. Although the pathogenesis of AMI is now preliminarily understood, there is still a lack of biomarkers that can accurately and rapidly diagnose its disease characteristics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study analyzed single\u2010cell RNA sequencing (scRNA\u2010seq) and bulk RNA\u2010seq data related to AMI from the Gene Expression Omnibus (GEO) database. Data preprocessing and clustering were performed using Seurat, and cell\u2013cell communication was analyzed using CellChat. Functional enrichment was performed using clusterProfiler. Key transcription factors were identified using SCENIC, and module\u2010specific genes associated with macrophages in AMI were identified using high\u2010dimensional weighted gene coexpression network analysis (hdWGCNA). These were combined with WGCNA to identify genes associated with AMI. Molecular docking was then used to predict potential targeted drugs for AMI. In addition, an oxygen\u2013glucose deprivation (OGD)\u2013induced AC16 cardiomyocyte model combined with quantitative real\u2010time polymerase chain reaction (qRT\u2013PCR) was used to validate the expression patterns of the key regulators and biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      This study identified six cell types in AMI, including adipocytes, cardiomyocytes, endothelial cells, fibroblasts, macrophages, and smooth muscle cells. CellChat showed that cell\u2013cell communication intensity was generally enhanced in AMI. hdWGCNA identified the M5 and M8 modules as significantly associated with macrophages. SCENIC analysis found that\n                      <jats:italic>FOS<\/jats:italic>\n                      and\n                      <jats:italic>ETV6<\/jats:italic>\n                      were important regulatory factors of macrophages. WGCNA screened the brown module as significantly associated with AMI, enriched in immune and inflammatory response pathways. The final integration of all analysis results identified\n                      <jats:italic>GAS7<\/jats:italic>\n                      and\n                      <jats:italic>ZEB2<\/jats:italic>\n                      as potential biomarkers for AMI, and receiver operating characteristic (ROC) curves validated the good diagnostic performance of the two genes for AMI. Furthermore, qRT\u2013PCR in an OGD\u2010induced AC16 cardiomyocyte model confirmed the upregulation of\n                      <jats:italic>FOS<\/jats:italic>\n                      ,\n                      <jats:italic>ETV6<\/jats:italic>\n                      ,\n                      <jats:italic>GAS7<\/jats:italic>\n                      , and\n                      <jats:italic>ZEB2<\/jats:italic>\n                      , consistent with the transcriptomic findings. Molecular docking indicated that\n                      <jats:italic>GAS7<\/jats:italic>\n                      has a good binding affinity with sulforaphane.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study revealed the cellular communication characteristics in the pathogenesis of AMI and identified\n                      <jats:italic>GAS7<\/jats:italic>\n                      and\n                      <jats:italic>ZEB2<\/jats:italic>\n                      as potential biomarkers for AMI based on macrophage characteristics. It also elucidated their targeted therapeutic drugs, providing new insights into the mechanism and treatment of AMI.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/humu\/5486574","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T07:22:41Z","timestamp":1778656961000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Role of Macrophage\u2010Related Genes GAS7 and ZEB2 in Acute Myocardial Infarction 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