Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery.

Abstract

Background: Sepsis, a critical infectious disease, is intricately linked to the dysfunction of the intracellular Golgi apparatus. This study aims to explore the relationship between sepsis and the Golgi apparatus using bioinformatics, offering fresh insights into its diagnosis and treatment.

Methods: To explore the role of Golgi-related genes in sepsis, we analyzed mRNA expression profiles from the NCBI GEO database. We identified differentially expressed genes (DEGs). These DEGs, Golgi-associated genes obtained from the MSigDB database, and key modules identified through WGCNA were intersected to determine Golgi-associated differentially expressed genes (GARGs) linked to sepsis. Subsequently, functional enrichment analyses, including GO, KEGG, and GSEA, were performed to explore the biological significance of the GARGs.A PPI network was constructed to identify core genes, and immune infiltration analysis was performed using the ssGSEA algorithm. To further evaluate immune microenvironmental features, unsupervised clustering was applied to identify immunological subgroups. A diagnostic model was developed using logistic regression, and its performance was validated using ROC curve analysis. Finally, key diagnostic biomarkers were identified and validated across multiple datasets to confirm their diagnostic accuracy.

Results: By intersecting DEGs, WGCNA modules, and Golgi-related gene sets, 53 overlapping GARGs were identified. Functional enrichment analysis indicated that these GARGs were predominantly involved in protein glycosylation and Golgi membrane-related processes. PPI analysis further identified eight hub genes: B3GNT5, FUT11, MFNG, ST3GAL5, MAN1C1, ST6GAL1, C1GALT1C1, and GALNT14. Immune infiltration analysis revealed significant differences in immune cell populations, mainly activated dendritic cells, and effector memory CD8⁺ T cells, between sepsis patients and healthy controls. A diagnostic model constructed using five pivotal genes (B3GNT5, FUT11, MAN1C1, ST6GAL1, and C1GALT1C1) exhibited predictive accuracy, with AUC values exceeding 0.96 for all genes. Validation with an independent dataset confirmed the differential expression patterns of B3GNT5, C1GALT1C1, and GALNT14, reinforcing their potential as robust diagnostic biomarkers for sepsis.

Conclusion: This study elucidates the link between sepsis and the Golgi apparatus, introduces novel biomarkers for sepsis diagnosis, and offers valuable insights for future research on its pathogenesis and treatment strategies.