Shock (Augusta, Ga.) · 2026
Identification of Molecular Subtypes and Prognostic Characteristics in Sepsis Based on T-Helper 17 Cell Differentiation-Related Genes.
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Li Xiuhua, Tan Lifei, Ding Yingwei, Zhang Bingwen
Аннотация
BACKGROUND: Sepsis, involving systemic inflammation and organ failure, presents significant challenges due to its complex and heterogeneous immunological reactions. T-helper 17 (Th17) cells contribute significantly to immune regulation, and their dysregulation is implicated in sepsis pathogenesis. Understanding how Th17 cell differentiation-related genes contribute to sepsis heterogeneity and prognosis is crucial for improving patient prognosis.
METHODS: We retrieved mRNA expression and clinical datasets (GSE65682, GSE95233, and GSE28750) from the Gene Expression Omnibus repository for subsequent analyses, and Th17 cell differentiation-related genes were obtained from the Molecular Signatures Database (MSigDB). We applied Weighted Gene Coexpression Network Analysis to identify sepsis-connected gene modules. Consensus clustering divided sepsis patients into subtypes. We established a prognostic model using Least Absolute Shrinkage and Selection Operator followed by multivariate Cox proportional hazards analysis. Immune infiltration, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were applied to characterize subtypes and risk groups.
RESULTS: Weighted Gene Coexpression Network Analysis identified a sepsis-associated turquoise module. Our analysis revealed the existence of two distinct molecular categories within sepsis, named Cluster 1 and Cluster 2, with Cluster 2 showing significantly poorer survival. We formulated a reliable eight-gene prognostic model, including DENND2D, CD74, BCL11A, FCER1A, LTB, TGFBI, ERAP2, and VSIG4, that effectively categorized patients into high- and low-risk groups. The low-risk group exhibited higher immune cell infiltration and enrichment in immune-related pathways, while the high-risk group showed enrichment in metabolic and stress pathways.
CONCLUSIONS: Our investigation identified novel Th17 cell differentiation-connected molecular subtypes of sepsis and established a reliable prognostic model. These observations shed light on the molecular variability associated with sepsis and hold the prospect of personalized therapeutic strategies and improved patient management.
METHODS: We retrieved mRNA expression and clinical datasets (GSE65682, GSE95233, and GSE28750) from the Gene Expression Omnibus repository for subsequent analyses, and Th17 cell differentiation-related genes were obtained from the Molecular Signatures Database (MSigDB). We applied Weighted Gene Coexpression Network Analysis to identify sepsis-connected gene modules. Consensus clustering divided sepsis patients into subtypes. We established a prognostic model using Least Absolute Shrinkage and Selection Operator followed by multivariate Cox proportional hazards analysis. Immune infiltration, gene set enrichment analysis, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were applied to characterize subtypes and risk groups.
RESULTS: Weighted Gene Coexpression Network Analysis identified a sepsis-associated turquoise module. Our analysis revealed the existence of two distinct molecular categories within sepsis, named Cluster 1 and Cluster 2, with Cluster 2 showing significantly poorer survival. We formulated a reliable eight-gene prognostic model, including DENND2D, CD74, BCL11A, FCER1A, LTB, TGFBI, ERAP2, and VSIG4, that effectively categorized patients into high- and low-risk groups. The low-risk group exhibited higher immune cell infiltration and enrichment in immune-related pathways, while the high-risk group showed enrichment in metabolic and stress pathways.
CONCLUSIONS: Our investigation identified novel Th17 cell differentiation-connected molecular subtypes of sepsis and established a reliable prognostic model. These observations shed light on the molecular variability associated with sepsis and hold the prospect of personalized therapeutic strategies and improved patient management.