Exploring the prognostic and diagnostic value of lactylation-related genes in sepsis

Abstract

The discovery of Lactylation may pave the way for novel approaches to investigating sepsis. This study focused on the prognostic and diagnostic significance of lactylated genes in sepsis. RNA sequencing was performed on blood samples from 20 sepsis patients and 10 healthy individuals at Southwest Medical University in Luzhou, Sichuan, China. Genes associated with sepsis were identified through analysis of RNA sequencing data. Afterward, the genes that were expressed differently were compared with the lactylation genes, resulting in the identification of 55 lactylation genes linked to sepsis. The overlapping genes underwent analysis using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-Protein Network Interactions were used to screen for the core genes. The datasets GSE65682, GSE69528, GSE54514, GSE63042, and GSE95233 were obtained from the GEO database to validate core genes. Survival analysis evaluated the predictive significance of central genes in sepsis, while Receiver Operating Characteristic (ROC) Curve analysis was employed to establish the diagnostic value of genes. Additionally, a meta-analysis was conducted to confirm the precision of RNA-seq data. We obtained five peripheral blood samples, including two from healthy individuals, one from a patient with systemic inflammatory response syndrome (SIRS), and two from patients with sepsis. These samples were used to identify the specific location of core genes using 10×single-cell sequencing. High-throughput sequencing and bioinformatics techniques identified two lactylation-related genes (S100A11 and CCNA2) associated with sepsis. Survival analysis indicated that septic patients with reduced levels of S100A11 had a decreased 28-day survival rate compared to those with elevated levels. Conversely, individuals exhibiting decreased CCNA2 levels demonstrated a greater likelihood of surviving for 28 days than those in the high expression category, indicating a favorable association with survival rates among sepsis patients (P < 0.05). Both genes showed high sensitivity and specificity based on the ROC curve, with AUC values of 0.961 for S100A11 and 0.890 for CCNA2. The meta-analysis revealed that S100A11 exhibited high levels of expression in the sepsis survivors, whereas it displayed low levels of expression in the non-survivors; on the other hand, CCNA2 demonstrated low expression in the sepsis survivors and high expression in the non-survivors (P < 0.05). Single-cell RNA sequencing ultimately showed that monocyte macrophages, T cells, and B cells exhibited high expression levels of the crucial genes associated with sepsis-induced lactylation. In conclusion, the lactylation genes S100A11 and CCNA2 are strongly linked to sepsis and could be valuable markers for diagnosing, predicting outcomes, and providing guidance for sepsis.

Keywords: Lactylation; Sepsis; Single-cell RNA sequencing technology; mRNA-seq.

Fig. 1

Illustrates the research workflow. Here, Sepsis denotes the sepsis group, and NC refers to the normal control volunteer group. PCA stands for Principal Component Analysis, GO represents Gene Ontology, and KEGG refers to the Kyoto Encyclopedia of Genes and Genomes. PPI signifies the Protein-Protein Interaction network, while scRNA-seq represents single-cell RNA sequencing technology.

Fig. 2

Screening for lactylation genes related to sepsis. (A)PCA is also known as Principal Component Analysis. Principal Component 1 is on the x-axis, while Principal Component 2 is on the y-axis. Each point represents a sample. (B)Volcano plot displaying genes with differential expression. Fold change is represented on the x-axis, while the negative logarithm of the P value is represented on the y-axis. The color green indicates genes that are upregulated and differentially expressed (n = 2498), while red indicates downregulated and differentially expressed (n = 2392). (C)Distribution plot showing differentially expressed genes, with green indicating upregulated genes and red indicating downregulated genes. (D)In the Venn diagram, blue represents lactylation genes, and red represents sepsis differentially expressed genes, overlapping 55 genes.

Fig. 3

Cross-genetic analysis of GO and KEGG. (A) GO enrichment analysis. Three enrichment results are shown on the horizontal axis, with the number of enriched genes on the right vertical axis and the percentage of enriched genes on the left horizontal axis. (B) KEGG analysis. The percentage of enriched genes is shown on the horizontal axis, with the primary categories of enrichment displayed on the right side and the secondary categories within the primary categories shown on the left vertical axis.

Fig. 4

PPI network diagram. The sepsis differential gene and the lactylation cross-gene were analyzed for PPI. The results show that GAPDH, S100A11, H2BC14, PARP1, TP53, CCNA2, NCL, S100A4, and H2BC13 are in the core of the network.

Fig. 5

Core gene survival analysis and ROC curve. (A-B) Based on the GEO database GSE65682, survival curves were plotted. The red line indicates the high-expression group, whereas the green line represents the low-expression group. The survival rate is depicted on the vertical axis, while the horizontal axis shows 28 days for survival. Patients with low expression of S100A11 had a lower 28-day survival rate compared to those with high expression, indicating a negative association with the survival rate of septic patients (P < 0.05). Patients with low CCNA2 expression had a higher 28-day survival rate than those with high expression, indicating a positive association with septic patient survival (P < 0.05). (C-D) ROC curves based on the GEO database GSE69528. Specificity is represented on the horizontal axis, while sensitivity is on the vertical axis. The findings suggest that S100A11 and CCNA2 show high levels of sensitivity and specificity, achieving AUC values of 0.961 and 0.890, respectively.

Fig. 6

According to the meta-analysis of the GEO databases GSE54514, GSE63042, and GSE95233, S100A11 exhibited high expression levels in the sepsis survival group. Still, it was low in the non-survival group, whereas CCNA2 displayed low expression in the sepsis survival group but high in the non-survival group. The disparities showed statistical significance with a P value less than 0.05.

Fig. 7

Single-cell spatial map of core genes. (A)The two-dimensional t-SNE plot after PCA dimensionality reduction, where each small dot represents a cell. T cell lineages are represented by clusters 1, 2, 6, and 8; NK cell lineages are represented by cluster 4; monocyte-macrophage lineages are represented by clusters 3 and 5; B cell lineage is represented by cluster 7; and platelets are represented by cluster 9. (B)Violin plots for gene expression. The vertical axis shows the ratio of cells expressing a gene in a specific cell line. Each cell cluster’s expression status is represented on the x-axis. (C-D) The expression distributions of CCNA2 and S100A11 in human blood PBMCs.