An immune-related gene signature predicts the 28-day mortality in patients with sepsis

• DOI: 10.3389/fimmu.2023.1152117
• Publication Date: 2023-03-23T07:20:37Z
• Authors: Yaojun Peng; Qiyan Wu; Hongyu Liu; Jinying Zhang; Qingru Han; Fan Yin; Lingxiong Wang; Qi Chen; Fei Zhang; Cong Feng; Haiyan Zhu
• Keywords: Gene Expression Profiling; Immunology; Genes, MHC Class II; Histocompatibility Antigens Class II; RC581-607; 3. Good health; sepsis; Sepsis; immune-related genes; Humans; Cytokines; transcriptomic profile; prognosis; Immunologic diseases. Allergy; integrative analysis

Introduction Sepsis is the leading cause of death in intensive care units and characterized by multiple organ failure, including dysfunction immune system. In present study, we performed an integrative analysis on publicly available datasets to identify immune-related genes (IRGs) that may play vital role pathological process sepsis, based which a prognostic IRG signature for 28-day mortality prediction patients with sepsis was developed validated. Methods Weighted gene co-expression network (WGCNA), Cox regression least absolute shrinkage selection operator (LASSO) estimation were used functional IRGs construct model predicting mortality. The value validated internal external datasets. correlations immunological characteristics, cell infiltration cytokine expression, explored. We finally expression three blood samples from 12 healthy controls using qPCR. Results established comprising members ( LTB4R , HLA-DMB IL4R ). demonstrated good predictive performance validation analyses revealed significantly associated cells cytokines. molecular pathway uncovered ontology enrichment myeloid differentiation iron ion homeostasis, providing clues regarding underlying biological mechanisms signature. Finally, qPCR detection verified differential controls. Discussion This study presents innovative patients, be facilitate stratification risky evaluate patients’ state.