Cuproptosis-Related Genes as Prognostic Biomarkers for Sepsis: Insights into Immune Function and Personalized Immunotherapy

Abstract

Background: This study aimed to discover diagnostic and prognostic biomarkers for sepsis immunotherapy through analyzing the novel cellular death process, cuproptosis.

Methods: We used transcriptome data from sepsis patients to identify key cuproptosis-related genes (CuRGs). We created a predictive model and used the CIBERSORT algorithm to observe the link between these genes and the septic immune microenvironment. We segregated sepsis patients into three subgroups, comparing immune function, immune cell infiltration, and differential analysis. Single-cell sequencing and real-time quantitative PCR were used to view the regulatory effect of CuRGs on the immune microenvironment and compare the mRNA levels of these genes in sepsis patients and healthy controls. We established a sepsis forecast model adapted to heart rate, body temperature, white blood cell count, and cuproptosis key genes. This was followed by a drug sensitivity analysis of cuproptosis key genes.

Results: Our results filtered three key genes (LIAS, PDHB, PDHA1) that impact sepsis prognosis. We noticed that the high-risk group had poorer immune cell function and lesser immune cell infiltration. We also discovered a significant connection between CuRGs and immune cell infiltration in sepsis. Through consensus clustering, sepsis patients were classified into three subgroups. The best immune functionality and prognosis was observed in subgroup B. Single-cell sequencing exposed that the key genes manage the immune microenvironment by affecting T cell activation. The qPCR results highlighted substantial mRNA level reduction of the three key genes in the SP compared to the HC. The prediction model, which combines CuRGs and traditional diagnostic indicators, performed better in accuracy than the other markers. The drug sensitivity analysis listed bisphenol A as highly sensitive to all the key genes.

Conclusion: Our study suggests these CuRGs may offer substantial potential for sepsis prognosis prediction and personalized immunotherapy.

Keywords: cuproptosis; immune cell infiltration; nomogram; prognosis; sepsis.

Figure 1

Difference analysis of CuRGs between healthy and sepsis groups. (A) Difference analysis of healthy and sepsis groups in GSE65682. The cutoff for selecting DEGs is set at P<0.05. (B) Identification of DE-CuRGs. (C) PPI network displays the relationship between 10 CuRGs. (D) Expression profile of 10 CuRGs in the healthy and sepsis groups. ***P<0.001; ns: P>0.05.

Figure 2

Developing a prognostic model for predicting the outcome of sepsis related to CuRGs. (A) The network showed the correlation and prognostic value of CuRGs. (B) Multivariate Cox analysis. (C) A cluster heatmap illustrating the gene expression level of PDH1, LIAS, and PDHB. (D and E) Risk subgroup classification and analysis of clinical survival outcomes at 28 days. (F) Principal Component Analysis (PCA) plot displaying the distribution of sepsis in different risk subgroups. (G) A comparison of the survival curves between the high-risk and low-risk groups. (H) ROC analysis of 7-, 14-, and 28 days.

Figure 3

Validation of the CuRGs prognostic model in the training and validation sets. (A and B) Stratification of high and low CuRG samples. (C) Expression profiling analysis of three independent prognostic factors in the training set. (D) Time-dependent ROC curve analysis in the training set. (E) Expression profiling analysis of three independent prognostic factors in the validation set. (F) Time-dependent ROC curve analysis in the validation set. (G and H) Evaluation of the 28-day clinical survival outcomes of sepsis samples in the training and validation sets.

Figure 4

Association of CuRGs risk score and immune infiltration. (A) Immune function infiltration landscape and of sepsis in CuRGs risk subgroups. (B) Immune function analysis between the low- and the high-risk groups. (C) Immune Score between the low- and the high-risk groups. (D) Relationship between CuRG risk score and 23 immune cells. (E) Relationship between CuRG risk score and immune cells. (F) Correlation analysis of 3 prognostic factors and immune infiltration. *P<0.05; **P<0.01; ***P<0.001; ns: P>0.05.

Figure 5

Molecular subgroup and clinical prognosis analysis of CuRGs for sepsis. (A) Consensus clustering analysis. (B) 28 days clinical survival outcome of sepsis in cluster subgroups. (C) PCA analysis of cluster A, cluster B, and cluster C based on DE-CuRGs. (D) Relationship of DE-CuRGs expression and clinical features in cluster A, cluster B and cluster C for sepsis. (E) CuRG score value of cluster A, cluster B, and cluster C subgroups. (F) Relationship between risk score, cluster, CuRG score, and clinical survival status.

Figure 6

Immune analysis and differential enrichment analysis of CuRGs molecular subtypes in sepsis. (A) Immune scores of CuRGs molecular subtypes in sepsis. (B) Functional analysis of immune functions in CuRGs molecular subtypes in sepsis. (C) Infiltration analysis of immune cells in CuRGs molecular subtypes in sepsis. Differential genes of CuRGs molecular subtypes in sepsis were subjected to (D) GO analysis and (E) KEGG enrichment analysis. *P<0.05; **P<0.01; ***P<0.001; ns: P>0.05.

Figure 7

Key genes of CuRGs in single-cell sequencing analysis of PBMCs. (A) Expression levels of key genes in major groups of PBMCs cells. (B) UMAP plots showing T cell clusters with high and low expression of key genes. (C) Heatmap of differential genes in T cell clusters with high and low expression of key genes. (D) GO enrichment analysis and (E) KEGG pathway enrichment for T cell clusters with high and low expression of key genes.

Figure 8

Independent prognostic analysis of CuRGs in sepsis. (A) Univariate and (B) Multivariate analysis revealed that CuRGs score is an independent prognostic factor for sepsis. (C) A prognostic nomogram model was constructed based on the CuRGs key genes. (D) ROC curve of the prognostic nomogram.

Figure 9

Construction of Prediction Nomogram Model and Drug Sensitivity of CuRGs in Sepsis. (A) Normalized gene expression of the three key genes between healthy controls (HC) and sepsis patients (SP) using qPCR. (B) Construction of a prediction nomogram model based on multivariate indicators. (C) ROC curve of the multivariate indicators. (D) Pearson’s correlation analysis between the proportion of immune cells in blood routine and the three hub genes. (E) Molecular docking analysis of bisphenol A with LIAS (F), PDHA1 (G), and PDHB (H). *P<0.05; **P<0.01; ****P<0.0001.