Anwaier Apizi,* Jian Li,* Paiheriding Kamilijiang, Chun-Bo Yang, Zheng-Kai Wang, Rui-Feng Chai, Zhao-Xia Yu

Department of Intensive Care Unit, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Zhao-Xia Yu, Department of Intensive Care Unit, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China, Tel +86 13899858397, Email zhaoxia_yu01@126.com Rui-Feng Chai, Department of Intensive Care Unit, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People’s Republic of China, Tel +86 13999819217, Email chairuifeng_chai@126.com

Objective: This study aimed to evaluate the extent of organ damage associated with sepsis and to identify key genes implicated in its pathogenesis.
Methods: Eighteen rats were randomized into experimental and control groups. Cecal ligation and puncture induced sepsis in the experimental group, with lung and kidney inflammatory injury assessed at 12, 24, and 36 hours. Gene expression profiles of sepsis patients and healthy controls were obtained from Gene Expression Omnibus database. Weighted gene co-expression network analysis and bioinformatics identified sepsis-related pathways and core genes, constructing a predictive risk model. Immune cell composition was compared between groups, and correlations between core gene expression and immune cell populations were analyzed.
Results: The experimental group exhibited greater lung and kidney tissue damage at all time points compared to the control group, with severity increasing over time. Cross-analysis identified 505 core genes associated with sepsis. Gene Ontology enrichment analysis revealed that differentially expressed genes were predominantly enriched in biological processes, molecular functions, cellular components, and the hematopoietic cell lineage pathway. A sepsis risk model constructed using five key genes—CD8A, ITGAM, CXCL8, CCL5, and LCK—demonstrated high predictive accuracy. Notable differences in immune cell composition were observed, with a statistically significant variation in T cells CD4 naïve and activated dendritic cells between the sepsis and control groups (p < 0.05). Additionally, a positive correlation was identified between CXCL8 expression and the proportion of activated dendritic cells.
Conclusion: The severity of lung and kidney tissue damage in sepsis increased over time. The five identified sepsis-related genes have predictive value in assessing sepsis risk. Insights into the interactions between key genes and immune cell populations may contribute to improved clinical management of sepsis.

Keywords: GEO, immune cell infiltration, key genes, nomogram, sepsis