Fig 1.
Flowchart of This Study.
Table 1.
Comparison of Baseline Clinical Characteristics Between Sepsis and Non-Sepsis Groups.
Fig 2.
Expression characteristics, diagnostic value, and correlation with immune cells of serum cholinesterase (CHE).
(A) Comparison of serum CHE activity between sepsis and non-sepsis groups. (B) ROC curve for CHE in diagnosing sepsis.
Table 2.
Comparison of Baseline Clinical Characteristics Between CHE < 5000 (U/L) and CHE > 5000 (U/L) in Sepsis Groups.
Table 3.
Inflammatory factor levels and immune cell counts on EICU admission stratified by CHE activity.
Fig 3.
Flowchart of the study design.
Fig 4.
Identification of candidate Hub DEGs and analysis of functional enrichment.
(A) Volcano plot of DEGs in the GSE137340 dataset. (B) Heat map of DEGs in the GSE137340 dataset. (C) Venn diagram of candidate hub genes. (D) Top 3 enriched GO biological-process (BP) terms of candidate hub genes. (E) Protein-Protein Interaction (PPI) network, Molecular Complex Detection (MCODE) module, and functional enrichment analysis of candidate hub genes.
Fig 5.
Identification of hub differentially expressed genes (DEGs) and analysis of functional enrichment.
(A, B) LASSO algorithm-based variable selection showing coefficient plots and partial likelihood deviance. (C) The Venn diagram showing overlapping genes leading to the identification of the hub gene RORA. (D, E) Volcano plot and heat maps of the DEGs between the high and low RORA expression groups. (F) The top 20 genes associated with RORA are shown. (G, H) GO and KEGG pathway analyses of the DEGs between the high and low RORA expression groups. (I, J) GSEA of the DEGs between the high and low RORA expression groups.
Fig 6.
Immune infiltration analysis and correlation with RORA.
(A) CIBERSORT analysis of 22 immune cell types in control and sepsis groups. (B) Differential immune cell infiltration between control (blue) and sepsis (pink) groups. (C) Correlation analyses between RORA and immune cell types. Point size represents correlation coefficient magnitude; colour indicates statistical significance. (D–I) Correlation analyses between RORA and CD8 + T cells, resting NK cells, naive CD4 + T cells, monocytes, activated dendritic cells, and M0 macrophages. (*P < 0.05; **P < 0.01; ***P < 0.001).
Fig 7.
Comparison of RORA expression levels between the sepsis group and the control group.
(A) Comparison of RORA expression levels between the sepsis group and the control group within the GSE137340 dataset. (B) Comparison of RORA expression levels between the sepsis group and the control group within the GSE95233 dataset. (C) Comparison of RORA expression levels between the sepsis group and the control group within the GSE72326 dataset. (D) Comparison of RORA expression levels between the sepsis group and the control group within the GSE69528 dataset. (E) Comparison of RORA expression levels between the sepsis group and the control group within the GSE28750 dataset. (F) Comparison of RORA expression levels between the sepsis group and the control group within the GSE13904 dataset. (G) Comparison of RORA expression levels between the sepsis group and the control group within the GSE25504 dataset. (H) Comparison of RORA expression levels between the sepsis group and the control group within the GSE145227 dataset.
Fig 8.
Diagnostic potential of RORA in sepsis.
(A) The diagnostic potential of RORA in sepsis within the GSE137340 dataset. (B)The diagnostic potential of RORA in sepsis within the GSE95233 dataset. (C)The diagnostic potential of RORA in sepsis within the GSE72326 dataset. (D)The diagnostic potential of RORA in sepsis within the GSE69528 dataset. (E)The diagnostic potential of RORA in sepsis within the GSE28750 dataset. (F)The diagnostic potential of RORA in sepsis within the GSE13904 dataset. (G)The diagnostic potential of RORA in sepsis within the GSE25504 dataset. (H)The diagnostic potential of RORA in sepsis within the GSE145227 dataset.
Fig 9.
Six cell types were annotated through scRNA-seq data analysis.
(A) UMAP dimensionality reduction clustering results and cell subpopulation annotations; (B) Distribution of cell subpopulations across two groups; (C) Bubble plot showing marker gene expression across the six cell types; (D) Cell proportion plots within different groups. (E, F) Enriched GO and KEGG pathways in each cell type.
Fig 10.
Communication between key cells and other cells and expression of key genes in different cells.
(A,B) Chord diagram depicting differences in the number and intensity of cell-cell communication interactions among sepsis cell subsets. (C) Heatmap depicting differences in the number and intensity of cell-cell communication interactions among sepsis cell subsets. (D) Bubble chart of cell communication. (E) Expression of RORA in different cells. (F) The expression levels of RORA was significantly different in two groups. (G) RORA subcellular localization.
Fig 11.
Flow cytometry measured the absolute counts of CD4 + T cells, CD8 + T cells, and CD3 + T cells.
(A, C, E) Representative flow cytometry contour plots of CD3+ (A), CD4+ (C), and CD8+ (E) T cells in the spleen from CLP mice (left) and Sham mice (right). (B, D, F) Quantification of absolute counts (×10³/mL) for spleen CD3+ (B), CD4+ (D), and CD8+ (F) T cells in CLP and Sham groups. Statistical significance: *P < 0.05, **P < 0.01, ***P < 0.001.
Fig 12.
Validation of CHE Concentration and RORA mRNA Expression in CLP Mice.
(A–C) Violin plots showing concentrations of serum IL-6 (A), IL-1β (B), and TNF-α (C) in CLP and Sham groups; (D) Violin plot of serum cholinesterase (CHE) activity in CLP and Sham groups; (E) Bar graph depicting relative mRNA expression of RORA in the spleen of Sham and CLP groups; (F) Correlation plot showing the relationship between RORA mRNA expression and CHE Concentration. (G-I) Correlation plot showing the relationship between CD4 ⁺ T, CD8 ⁺ T, CD3 ⁺ T cell counts and CHE concentration. Statistical significance: *P < 0.05, **P < 0.01, ***P < 0.001.