{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:12:45Z","timestamp":1786119165236,"version":"build-2736575974"},"reference-count":41,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Drug Development Research"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Sepsis, a life\u2010threatening condition triggered by dysregulated host response to infection, poses significant global health challenges. Identifying lipopolysaccharide (LPS)\u2010related biomarkers and underlying mechanisms remains critical, yet underexplored. We integrated bulk microarray datasets and single\u2010cell RNA\u2010seq data from the Gene Expression Omnibus to identify LPS\u2010related genes associated with sepsis. scRNA\u2010seq was used for cell clustering, annotation, AUCell scoring, and cell\u2010cell communication analysis. Differentially expressed LRGs were screened from both bulk and single\u2010cell datasets and intersected. Three machine learning algorithms\u2014least absolute shrinkage and selection operator regression, support vector machine\u2013recursive feature elimination, and extreme gradient boosting\u2014were applied to select robust diagnostic biomarkers. Gene expression was validated via qRT\u2010PCR. Diagnostic and prognostic models were constructed and validated in independent cohorts. Seven key LRGs were identified. The diagnostic model achieved high AUCs (&gt;\u20090.89) across validation cohorts, while the prognostic model effectively stratified patients into distinct survival groups. High\u2010risk groups showed increased myeloid\u2010derived suppressor cell and macrophage infiltration, activation of inflammatory pathways, and unique intercellular communication networks. scRNA\u2010seq revealed cell\u2010type\u2010specific LRGs expression, particularly in myeloid populations. We established and validated a robust LPS\u2010related biomarker model that integrates bulk microarray and single\u2010cell transcriptomics, offering novel diagnostic, prognostic, and therapeutic insights for sepsis.<\/jats:p>","DOI":"10.1002\/ddr.70241","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T16:25:37Z","timestamp":1770395137000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bulk Microarray and Single\u2010Cell Transcriptomic Analyses Reveal Bacterial Lipopolysaccharide\u2010Related Biomarkers in Sepsis"],"prefix":"10.1002","volume":"87","author":[{"given":"Haili","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine Shanghai Fourth People's Hospital, School of Medicine, Tongji University Shanghai 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