{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T05:09:59Z","timestamp":1778735399324,"version":"3.51.4"},"reference-count":18,"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"}],"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>Sepsis lacks reliable biomarkers for early diagnosis and treatment. This study integrates systems biology approaches, weighted gene coexpression network analysis (WGCNA), and experimental validation to identify novel diagnostic and therapeutic targets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Four sepsis\u2010related GEO datasets were analyzed to identify differentially expressed genes (DEGs) and coexpression modules. Machine learning algorithms screened candidate biomarkers from the intersection of DEGs and module hub genes, validated via ROC analysis and immune infiltration assessment. The biological function of the top candidate was verified in vitro using LPS\u2010stimulated THP\u20101 cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      <jats:italic>GALNT14<\/jats:italic>\n                      was identified as a robust diagnostic biomarker (AUC = 0.79), showing significant correlation with neutrophil and monocyte infiltration. In vitro validation confirmed\n                      <jats:italic>GALNT14<\/jats:italic>\n                      upregulation in the sepsis model. Functionally,\n                      <jats:italic>GALNT14<\/jats:italic>\n                      knockdown significantly inhibited proinflammatory cytokines (IL\u20101\n                      <jats:italic>\u03b2<\/jats:italic>\n                      , IL\u20106, and TNF\u2010\n                      <jats:italic>\u03b1<\/jats:italic>\n                      ), whereas its overexpression exacerbated the inflammatory response and modulated cell apoptosis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      Through a synergistic framework of AI\u2010driven bioinformatics and wet\u2010lab verification, this study identifies\n                      <jats:italic>GALNT14<\/jats:italic>\n                      as a promising diagnostic biomarker and therapeutic target, mechanistically linking it to the regulation of inflammatory responses in sepsis.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/humu\/1474506","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T04:22:44Z","timestamp":1778732564000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Systems Biology and Experimental Validation Unveil GALNT14 as a Novel Diagnostic Biomarker and Therapeutic Target for 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