Investigation of the Significance of Blood Signatures on Sepsis-Induced Acute Lung Injury in Sepsis Within 24 Hours

Abstract

Background: Sepsis is an infection-induced dysregulated cellular response that leads to multiorgan dysfunction. As a time-sensitive condition, sepsis requires prompt diagnosis and standardized treatment. This study investigated the impact of biomarkers identified in peripheral whole blood from sepsis patients (24-h post-onset) on sepsis-induced acute lung injury (ALI) using bioinformatics and machine learning approaches. Methods: Gene Expression Omnibus (GEO) datasets were analyzed for functional and differential gene expression. Critical genetic markers were identified and evaluated using multiple machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) and cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) were conducted to explore associations between biomarkers and immune cells. Biomarker expression was further validated through animal experiments. Result: A total of 611 overlapping differentially expressed genes (DEGs) were identified in GSE54514, including 361 upregulated and 250 downregulated genes. From GSE95233, 1150 DEGs were detected, with 703 upregulated and 447 downregulated genes. Enrichment analysis revealed DEGs associated with immune cell activity, immune cell activation, and inflammatory signaling pathways. Component 3a receptor 1 (C3AR1) and secretory leukocyte peptidase inhibitor (SLPI) were identified as critical biomarkers through multiple machine learning approaches. CIBERSORT analysis revealed significant associations between immune cell types and C3AR1/SLPI. Moreover, the scRNA-seq analysis demonstrated that the SLPI expression was significantly elevated in immunological organ cells during the early stages of sepsis, a finding further validated in sepsis-induced ALI models. Conclusion: This study employed machine learning techniques to identify sepsis-associated genes and confirmed the importance of SLPI as a biomarker within 24 h of sepsis onset. SLPI also played a significant role in sepsis-induced ALI, suggesting its potential as a novel target for personalized medical interventions, targeted prevention, and patient screening.

Keywords: ALI; SLPI; machine-learning strategy; sepsis.

Figure 1

Flowchart of the bioinformation.

Figure 2

Volcano plot and heat map. (a) Volcano plots of DEG distribution in GSE95233. (b) Heatmaps of DEGs in GSE95233. (c) Volcano plots of DEG distribution in GSE54514. (d) Heatmaps of DEGs in GSE54514.

Figure 3

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs). (a–d) Bubble charts show GO-enriched items of DEGs in three functional groups: biological processes (BP), cell composition (CC), and molecular function (MF). The x-axis labels represent gene ratios, and y-axis labels represent GO terms. The size of circle represents gene count. Different colors of circles represent different adjusted p values. (e) Circle plot shows KEGG-enriched items of DEGs. The height of the bar in the inner ring indicates the significance of the term, and color corresponds to the p value.

Figure 4

Screening of critical signatures via multiple machine learning. (a, b) Establishment of signatures by least absolute shrinkage and selection operator (LASSO) logistic regression analysis. LASSO coefficient profile of the 9 genes, and different colors represent different genes. Selection of the optimal parameter (lambda) in the LASSO model and generation of a coefficient profile plot. (c) Process of weighted gene coexpression network analysis (WGCNA). Analysis of network topology for various softthresholding powers. The x-axis reflects the soft-thresholding power. The y-axis reflects the scale-free topology model fit index and the mean connectivity. (d) Clustering dendrogram of differentially expressed genes related to sepsis, with dissimilarity based on topological overlap, together with assigned module colors. (e) Module–trait associations. Each row corresponds to a module, and each column corresponds to a trait. Each cell contains the corresponding correlation and p value. The table is color-coded by correlation according to the color legend. (f) Venn diagram shows the intersection of critical signatures obtained by the three strategies.

Figure 5

The expressions of C3AR1 and SLPI in (a) GSE28750, (b) GSE7065, (c) GSE69528, and (d) GSE95233.

Figure 6

Immune cell infiltration analysis and relationships between key signatures and immune cells in sepsis. (a) Heatmap of correlation in 17 types of immune cells. The size of the colored squares represents the strength of the correlation; red represents a positive correlation, and blue represents a negative correlation. Darker color implies stronger association. (b) Correlations between C3AR1, SLPI, and infiltrating immune cells. (c) UMAP visualization of clustering revealing 17 cell clusters. (d) Violin plots show expression distribution of C3AR1 and SLPI mRNA in different cell clusters in the spleen. Cluster identities: 0, T cells; 1, T cells; 2, endothelial cells; 3, smooth muscle cells; 4, macrophage; 5, monocyte; 6, chondrocytes; 7, monocyte; 8, B cell; 9, endothelial cells; 10, tissue stem cells; 11, smooth muscle cells; 12, B cell; 13, monocyte; 14, NK cell; 15, NA. (e) Distribution of C3AR1 and SLPI in immune cells.

Figure 7

CLP-induced lung pathological changes in mice. (a) Body weight in mice between the control and sepsis groups. (b) Survivals in mice between the control and sepsis groups. (c) W/D ratios in mice lung tissue. Lung wet/day ratios were measured at 24 h. (d–f) TNF-α, IL-6, and IL-1β expressions in mice between the control and sepsis groups. (g) Lung tissues were collected 24 h after the operation and examined by light microscopy after H&E staining. (h) Lung damage was evaluated using the lung scores described in the methods.

Figure 8

Expression of SLPI in mice lung tissues. (a, b) Plasma SLPI and C3AR1 expressions in mice between the control and sepsis groups. (c) Immunostaining for SLPI in lung tissues. (d) SLPI mRNA in mice between the control and sepsis groups. (e, f) Protein levels of SLPI in mouse lung and were assessed by Western blot analysis.