{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T23:04:27Z","timestamp":1788908667765,"version":"build-2803163510"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:00:00Z","timestamp":1768435200000},"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>Sepsis outcomes remain difficult to predict because immune trajectories are heterogeneous and dynamically shift from early activation to immunosuppression. Endotoxin tolerance (ET) in circulating monocytes\/macrophages is a key mechanism of sepsis-associated immunosuppression but has not been systematically leveraged for prognostication. We sought to develop and clinically validate an ET-related gene (ETG) signature for short-term mortality risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      Public whole-blood transcriptomic datasets were intersected with curated ET gene sets to derive ETG candidates. An ensemble machine-learning framework (108 model\/feature-selection combinations across 12 algorithms) was used to build and rank prognostic models for 28-day mortality; the final parsimonious signature (10 ETGs, including IL4R, ATM, CX3CR1, FCGR1A) informed a risk score. A two-variable nomogram (age + ETG risk score) was constructed. Internal performance was assessed by bootstrapped calibration, time-dependent ROC\/AUC at 7, 14, and 28 days, and Harrell\u2019s C-index. Experimental validation used a prospective ICU cohort (n=50; 13 survivors, 37 non-survivors). PBMCs were profiled by RT-qPCR and Western blot; CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes were analyzed by flow cytometry for FCGR1A\/CD64, CX3CR1, and PD-1. We further explored regulatory context (ceRNA network) and druggability (DGIdb query and molecular docking to CX3CR1, FCGR1A, and TLR5).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The 10-gene ETG signature stratified mortality risk with acceptable discrimination and calibration. AUCs were 0.76 (95% CI 0.69\u20130.83), 0.78 (0.72\u20130.83), and 0.73 (0.67\u20130.78) at 7, 14, and 28 days, respectively; Harrell\u2019s C-index was 0.782. The age-integrated nomogram showed close agreement between predicted and observed survival across timepoints. Functional enrichment indicated immune-response pathways enriched in the low-risk group. In the clinical cohort, non-survivors exhibited lower mRNA and protein levels of FCGR1A, TLR5, and CX3CR1 in PBMCs; flow cytometry revealed reduced proportions of FCGR1A\n                      <jats:sup>+<\/jats:sup>\n                      , CX3CR1\n                      <jats:sup>+<\/jats:sup>\n                      , and PD-1\n                      <jats:sup>+<\/jats:sup>\n                      CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes (p&amp;lt;0.01). The ceRNA analysis highlighted a putative NEAT1\/miR-1287-5p\/CX3CR1 axis. Docking suggested plausible ligandability of CX3CR1, FCGR1A, and TLR5, nominating candidates such as valproic acid and CGP-52608 for follow-up testing.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>An ET-anchored, 10-gene signature captures a clinically relevant axis of sepsis-associated immunosuppression and enables short-term mortality risk stratification. Integration with age yields a simple nomogram with stable internal performance. Multilayer validation (transcript, protein, single-cell) supports biological plausibility. Prospective multicenter studies with richer clinical annotation and functional assays are warranted to confirm generalizability and to evaluate ET-guided immunomodulatory strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2025.1707490","type":"journal-article","created":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:18:07Z","timestamp":1768454287000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification and validation of an endotoxin tolerance\u2013based prognostic model with therapeutic insights in sepsis"],"prefix":"10.3389","volume":"16","author":[{"given":"Yu","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Respiratory and Critical Medicine, University-Town Hospital of Chongqing Medical University","place":["Chongqing, 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Care Medicine, University-Town Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Medicine, University-Town Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Du","sequence":"additional","affiliation":[{"name":"Center for Mental Health, University-Town Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Medicine, University-Town Hospital of Chongqing Medical University","place":["Chongqing, 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