Immune-associated molecular classification and prognosis signature of sepsis

This study aims to explore the molecular subtypes of sepsis and the correlation between immune-related genes and the prognosis of patients with sepsis. Utilizing the Gene Expression Omnibus dataset (GSE65682) with 479 patients with sepsis as the training set and 164 patients treated at our hospital as the independent validation cohort. An unsupervised cluster analysis was used to identify potential molecular subtypes of sepsis, and a weighted gene co-expression network analysis was performed to identify gene modules. Gene Ontology, Kyoto Encyclopedia of Genes, and Genomes enrichment analyses were performed, and the immune status was also evaluated. Using LASSO regression and multivariate Cox regression, an immune-related gene prognostic model was developed, validated, and evaluated, followed by an individual risk scoring system. We identified two molecular subtypes of sepsis that are associated with distinct immune response patterns and clinical outcomes. Patients in Cluster A exhibited poorer survival and enrichment of pro-inflammatory pathways, while those in Cluster B had better outcomes and enrichment of immune regulatory pathways. A 10-gene prognostic model was constructed, stratifying patients into high- and low-risk groups using the estimated risk score that was confirmed to be an independent prognostic factor in both the training (hazard ratio [HR]: 1.126, 95% confidence interval [CI]: 1.096-1.156, P < 0.001) and validation datasets (HR: 1.149, 95% CI: 1.085-1.216, P < 0.001). A risk scoring system was developed based on the risk score and clinical parameters, with estimated mortality probabilities of 0.132 (7-day), 0.211 (14-day), and 0.258 (21-day). High-risk patients had significantly worse prognoses, and this was validated in the independent cohort. Distinct immune cell profiles were found between the two subtypes and risk groups, with B cells, CD8 + T cells, and NK cells elevated in Cluster B. This study identified immune-related molecular subtypes of sepsis and developed a prognostic model that accurately predicts sepsis mortality. These findings provide insights into the immune dysregulation in sepsis and can potentially be used for developing personalized treatment strategies and improving clinical decision-making in sepsis management.