The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data.

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. It remains a significant medical challenge due to its high mortality rates and requires a deeper understanding of its underlying mechanisms. This study aims to elucidate the differential expression of necroptosis-related genes in sepsis and their impact on immune characteristics.

METHODS

We obtained gene expression profiles and single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package, and functional enrichment analysis was performed using the clusterProfiler package for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were conducted to explore pathway enrichments. Immune cell infiltration differences between sepsis (SE) and healthy control (HC) groups were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Differential marker genes between SE and HC groups were identified by single-cell data analysis using the Seurat and SingleR packages.

RESULTS

Our results revealed 849 necroptosis-related DEGs, with 843 upregulated and 16 downregulated in the SE group. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified 22 key DEGs, including , , and . Among these, 157 necroptosis-related DEGs were consistently identified between SE and HC groups. GO analysis indicated significant enrichment in biological processes such as the regulation of apoptotic signaling pathways and IκB kinase/NF-κB signaling. KEGG pathway analysis revealed involvement in necroptosis, apoptosis, and NOD-like receptor signaling pathways. GSVA demonstrated that Wnt signaling was upregulated in the SE group. Significant differences in immune cell infiltration were observed between sepsis and healthy control groups, particularly in activated B cells and CD4 T cells. Single-cell RNA sequencing identified 33,287 cells categorized into 26 clusters, with neutrophils predominating. Key necroptosis genes such as , , , , , and exhibited differential expression patterns across various immune cell types.

CONCLUSIONS

Our integrated bioinformatics approach provides insights into the role of necroptosis-related genes in sepsis pathogenesis and their influence on immune responses. These findings improve our understanding of sepsis mechanisms and may guide future therapeutic strategies targeting necroptosis pathways.