{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T00:39:16Z","timestamp":1793839156556,"version":"4.6.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T00:00:00Z","timestamp":1753920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Inflammatory bowel disease (IBD) presents unpredictable therapeutic responses and complex immune dysregulation. Current precision medicine approaches lack robust molecular tools integrating transcriptomic signatures with immune dynamics for personalized treatment guidance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed multi-omics analyses of GEO datasets using machine learning algorithms (LASSO\/Random Forest) to derive a four-gene signature. Validation employed ten algorithms and nomogram construction. Immune infiltration (CIBERSORT\/ssGSEA), single-cell RNA sequencing, and DSS-colitis models characterized immune dynamics, cellular specificity, and therapeutic response modulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified 536 differentially expressed genes significantly enriched in IL-17 signaling, TNF signaling, and cytokine-cytokine receptor interactions. WGCNA revealed six co-expression modules with disease-specific correlations: turquoise module strongly correlated with Crohn\u2019s disease (r=0.6, P=4\u00d710\n                      <jats:sup>-20<\/jats:sup>\n                      ) and purple module with ulcerative colitis (r=0.55, P=1\u00d710\n                      <jats:sup>-16<\/jats:sup>\n                      ). The four-gene signature (CDC14A, PDK2, CHAD, UGT2A3) demonstrated exceptional diagnostic performance across ten validation algorithms (AUC range: 0.86-0.97), with the integrated nomogram achieving superior accuracy (AUC=0.952) compared to individual genes (CDC14A: 0.934, PDK2: 0.913, CHAD: 0.893, UGT2A3: 0.797). Consensus clustering stratified patients into two distinct molecular subtypes: Cluster 1 exhibited elevated M1 macrophages, activated dendritic cells, and neutrophils with enhanced glycolysis and mTORC1 signaling, while Cluster 2 showed higher signature gene expression, enhanced oxidative phosphorylation, and enrichment in regulatory immune populations including Tregs and M2 macrophages. Single-cell RNA sequencing revealed cell-type-specific expression patterns: PDK2 demonstrated widespread expression across epithelial cycling cells and stem cells, UGT2A3 showed preferential epithelial localization, and CDC14A exhibited selective enrichment in innate lymphoid cells. Nomogram-based risk stratification effectively predicted biologic treatment responses across multiple therapeutic classes using four independent treatment datasets (GSE16879, GSE92415, GSE73661, GSE206285): low-risk patients demonstrated superior response rates to golimumab (63.3%), infliximab (54.8%), and vedolizumab (29% vs. 15% in high-risk group). Connectivity Map analysis identified MS.275 as the top therapeutic enhancer, with experimental validation in DSS-induced colitis confirming synergistic anti-inflammatory effects with TNF-\u03b1 inhibitors, improving disease activity indices and restoring signature gene expression patterns.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This mechanistically grounded four-gene signature enables precise IBD patient stratification across distinct immunological subtypes and predicts biologic responses, providing validated molecular tools for precision immunotherapy and personalized treatment optimization.<\/jats:p>\n                  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