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Neutrophil extracellular traps (NETs) have been identified as critical drivers of various fibrotic diseases; however, their precise role and regulatory mechanisms in UC-associated intestinal fibrosis (UC-IF) remain to be systematically elucidated. Therefore, identifying the key regulatory genes through which NETs participate in UC-IF is highly important for the diagnosis and treatment of this condition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we analyzed the gene expression profiles of UC patients via publicly available databases and screened for neutrophil extracellular trap-related differentially expressed genes (NET-DEGs), identifying a total of 99 NET-DEGs. Enrichment analysis was then performed to explore the biological processes involving these genes. Furthermore, this study identified 5 potentially key NET-DEGs using machine learning algorithms. On the basis of these 5 genes, combined with protein-protein interaction (PPI) network analysis, intestinal fibrosis-related gene set variation analysis (GSVA), and immune infiltration analysis, we identified NET-related biomarkers associated with UC-IF. Finally, we established a mouse model of UC-IF in vivo to validate the bioinformatics findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This analysis revealed that IL1RN, MUC1, and SERPINA1 represent a novel panel of NET-related biomarkers for UC-IF. These genes were enriched in immune responses, interleukin signaling pathways, O-glycan biosynthesis, and mucosal barrier-related pathways. Furthermore, these genes were positively correlated with several immune cell types, including neutrophils, macrophages (M0 and M1), and dendritic cells, collectively mediating immune dysregulation in UC and thereby contributing to the pathogenesis of UC-IF. Notably, animal experiments further validated the aberrantly high expression of these genes in the intestinal tissues of UC-IF mice, which was consistent with the bioinformatics findings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>As UC-IF-associated NET biomarkers, IL1RN, MUC1 and SERPINA1 provide novel insights into the clinical diagnosis, treatment, and translational research of UC-IF.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12876-026-05179-5","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T06:24:11Z","timestamp":1785479051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identification of IL1RN, MUC1, and SERPINA1 as key NET-related biomarkers in ulcerative colitis-associated intestinal fibrosis via bioinformatics and experimental validation"],"prefix":"10.1186","volume":"26","author":[{"given":"Qiang","family":"Chuai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaixuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"5179_CR1","doi-asserted-by":"publisher","first-page":"115819","DOI":"10.1016\/j.intimp.2025.115819","volume":"168","author":"Y Li","year":"2026","unstructured":"Li Y, Wang Y, Chen S, Liu L. 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