Classification and functional analysis of disulfidptosis-associated genes in sepsis

Abstract

Sepsis represents a critical condition characterized by multiple-organ dysfunction resulting from inflammatory response to infection. Disulfidptosis is a newly identified type of programmed cell death that is intimately associated with the actin cytoskeleton collapse caused by glucose starvation and disulfide stress, but its role in sepsis is largely unknown. The study was to adopt a diagnostic and prognostic signature for sepsis with disulfidptosis based on the differentially expressed genes (DEGs) between sepsis and healthy people from GEO database. The disulfidptosis hub genes associated with sepsis were identified, and then developed consensus clustering and immune infiltration characteristics. Next, we evaluated disulfidptosis-related risk genes by using LASSO and Random Forest algorithms, and constructed the diagnostic sepsis model by nomogram. Finally, immune infiltration, GSVA analysis and mRNA-miRNA networks based on disulfidptosis-related DEGs were screened. There are five upregulated disulfidptosis-related genes and seven downregulated genes were filtered out. The six intersection disulfidptosis-related genes including LRPPRC, SLC7A11, GLUT, MYH9, NUBPL and GYS1 exhibited higher predictive ability for sepsis with an accuracy of 99.7%. In addition, the expression patterns of the critical genes were validated. The study provided a comprehensive view of disulfidptosis-based signatures to predict the prognosis, biological features and potential treatment directions for sepsis.

Keywords: bioinformatics; disulfidptosis; gene expression; immune infiltration; sepsis.

FIGURE 1

Flow chart.

FIGURE 2

Visualization of three sepsis datasets before and after batch and normalization. (A, B) Datasets before batch and normalization. (C, D) Datasets after batch and normalization. (E) Volcano plot of sepsis‐related DEGs in which red nodes indicate upregulated, green nodes indicate downregulated, and black nodes indicate genes are not DEGs. (F) Heat map of sepsis‐related DEGs expressions: blue (type N) indicates normal samples, pink (type T) indicates disease sample, red and green indicates high and low expression, respectively. (G) Violin chart showing differences in immune infiltration between sepsis patients and healthy people. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 3

Differential expression, correlation and NMF clustering of genes associated with disulfide poisoning in sepsis. (A) Heatmap display the expression of 12 disulfidptosis‐related DEGs. (B) The relative positions of the 12 disulfidptosis‐ related DEGs. (C) NMF consensus clustering for k = 2. (D) Heatmap of Coefficient matrix. (E) Heatmap of Basis matrix. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 4

Identification of disulfidptosis subtypes and immune signature. (A) Disulfidptosis‐associated cluster. (B) Representative cumulative distribution function (CDF) curve. (C) Representative CDF delta area curve. (D) A principal component analysis (PCA) visualization of the cluster distributions. (E) Two disulfidptosis clusters of 17 disulfidptosis‐related genes were represented in the heatmap. (F) Boxplots showing differences in the expression of 17 disulfidptosis‐related genes between the two disulfidptosis clusters. (G) Boxplots display the comparison of immune infiltration in two disulfidptosis clusters. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 5

Explore the gene clusters based on the DEGs of disulfidptosis clusters. (A) Based on 38 DEGs, the consensus clustering matrix for k = 2. (B) CDF curve. (C) CDF delta area curve. (D) PCA visualization of the distribution of the two gene clusters. (E) Heatmap of the 38 DEGs between disulfidptosis clusters. (F) Boxplots showing the disulfidptosis‐associated DEGs in the two gene clusters. (G) Relative abundance of immune cells between the two gene clusters. (H–I) There are statistically significant differences in the disulfidptosis score both in disulfidptosis clusters (H) and disulfidptosis‐related DEGs cluster (I). *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 6

Construction and validation of the least absolute shrinkage and selection operator (LASSO) model and Random Forest (RF) model. (A) The influence of the number of decision trees on the error rate. (B) Results of the Gini coefficient method in the random forest classifier. (C, D) Screening of disulfidptosis‐related differentially expressed genes (DEGs) signature using the LASSO algorithm. (E) The ROC curve of the mRNA in the model (LRPPRC, SLC7ALL, GYS1, NUBPL, MYH9, GLUT). (F) The AUC of the constructed model (AUC = 0.997, 95% CI:0.989–1.000). (G) The ordinary nomogram for the joint diagnosis of sepsis is based on LRPPRC, SLC7A11, GLUT, MYH9, NUBPL, and GYS1. (H) Calibration curve for nomogram validation. (I, J) The clinical impact curve and decision curve analysis are based on the nomogram model.

FIGURE 7

Immune infiltration analysis of the disulfidptosis‐related genes. (A) Heatmap showing the correlation between disulfidptosis‐related genes and the different immune cells. (B–G) The expression level of genes in the signature and the infiltration abundance of immune cells. *p < 0.05, **p < 0.01, ***p < 0.001.

FIGURE 8

GSVA analysis showing the different KEGG pathways related to six model genes. (A) GLUT. (B) GYS1. (C) LRPPRC. (D) MYH9. (E) NUBPL. (F) SLC7A11. Green indicates down‐regulated; red indicates up‐regulated; gray indicates not.

FIGURE 9

MRNA‐miRNA regulatory network with potential regulatory relationships with six genes in the model.

FIGURE 10

Validation of murine sepsis model. (A) Represented HE staining among control and LPS groups. (B) The mRNA expression levels of LRPPRC, NUBPL, GYS1, MYH9, GLUT1, and SLC7A11 in heart, lung, liver, and kidney. (C) Western blot for SLC7A11. (D) Representative immunofluorescence images for SLC7A11. *p < 0.05, **p < 0.01.