Bulk Microarray and Single-Cell Transcriptomic Analyses Reveal Bacterial Lipopolysaccharide-Related Biomarkers in...

Sepsis, a life-threatening condition triggered by dysregulated host response to infection, poses significant global health challenges. Identifying lipopolysaccharide (LPS)-related biomarkers and underlying mechanisms remains critical, yet underexplored. We integrated bulk microarray datasets and single-cell RNA-seq data from the Gene Expression Omnibus to identify LPS-related genes associated with sepsis. scRNA-seq was used for cell clustering, annotation, AUCell scoring, and cell-cell communication analysis. Differentially expressed LRGs were screened from both bulk and single-cell datasets and intersected. Three machine learning algorithms-least absolute shrinkage and selection operator regression, support vector machine-recursive feature elimination, and extreme gradient boosting-were applied to select robust diagnostic biomarkers. Gene expression was validated via qRT-PCR. Diagnostic and prognostic models were constructed and validated in independent cohorts. Seven key LRGs were identified. The diagnostic model achieved high AUCs (> 0.89) across validation cohorts, while the prognostic model effectively stratified patients into distinct survival groups. High-risk groups showed increased myeloid-derived suppressor cell and macrophage infiltration, activation of inflammatory pathways, and unique intercellular communication networks. scRNA-seq revealed cell-type-specific LRGs expression, particularly in myeloid populations. We established and validated a robust LPS-related biomarker model that integrates bulk microarray and single-cell transcriptomics, offering novel diagnostic, prognostic, and therapeutic insights for sepsis.