Risk model based on neutrophil-related genes constructs to assess prognosis and immune landscape in sepsis

Background: Sepsis is a persistent systemic inflammatory disease involving multiple organ failure caused by a dysregulated immune response to infection. As primary effector cells in innate immunity, neutrophils significantly contribute to combating infections and mediating inflammatory responses. The aim of this study was to evaluate the prognostic significance of neutrophil-related genes (NRGs) in sepsis and their relationship with the immune microenvironment. Methods: This study was drawing on transcriptomic data and clinical characterization of sepsis patients from the GEO database. Sepsis prognostic genes were discovered through a combination of differential analysis, weighted gene co-expression network analysis (WGCNA), and univariate cox regression analysis. Molecular subtypes of sepsis were identified by consensus clustering methods, survival differences between subtypes were compared and gene enrichment analysis was performed. Further univariate cox regression analysis and LASSO combined were performed to identify sepsis feature genes. Utilizing multivariate cox regression analysis, a prognostic model was developed, and its predictive capability was subsequently examined through receiver operating characteristic (ROC) curve and Kaplan-Meier (K-M) survival analyses. Subsequently, immune cell infiltration and gene enrichment were assessed in the various risk groups. Results: This study identified the presence of two molecular subtypes in sepsis, with Cluster1 patients having significantly better survival than Cluster2. The prognostic model based on NRGs (RCBTB2, KRT23, KLF9, GIMAP4 and CD84) in this study significantly differentiated the survival prognosis of high risk and low risk patients. The low risk patient group showed significant enrichment in immune related pathways (T cell receptor signaling and primary immunodeficiency), while the high risk group primarily exhibited significant enrichment in metabolism related pathways (glycine serine and threonine metabolism). Conclusion: Risk models constructed on the basis of NRGs are effective in predicting survival outcomes and immune profiles of sepsis patients, providing a new perspective on the link between NRGs and sepsis.