Abstract
Sepsis is a life-threatening organ malfunction induced by an imbalanced immunological reaction to infection in the host. Many studies have utilized traditional RNA sequencing (RNA-seq) data to identify important biological targets to predict sepsis prognosis. However, alterations in core cells and functional status cannot be effectively detected in sepsis patients. The goal of this study was to identify key cells through single-cell RNA-seq (scRNA-seq), and combine bulk RNA-seq data and multiple algorithm analysis to construct a stable prognostic model for sepsis. The scRNA-seq and bulk RNA-seq data from sepsis patients were collected from the Gene Expression Omnibus (GEO) database. The R package "Seurat" was used to process the scRNA-seq data. Cell communication was investigated using the R package "CellChat". The pseudo-time of the cells was calculated using the R package "monocle". The R package "limma" was used to identify differentially expressed genes (DEGs) between the sepsis group and the control group. Weighted gene correlation network analysis (WGCNA) was used to identify critical modules. Eight kinds of machine learning and 90 algorithm combinations were used to construct the prognostic model for sepsis. Quantitative real-time PCR (qRT‒PCR) was performed to determine the expression of key genes in the cecal ligation and puncture (CLP)-induced sepsis mouse model. The immunological status and related properties of DEGs were then investigated in the high- and low-risk groups delineated by the model. By combining the scRNA-seq data from nine samples, 13 clusters and 9 cell types were identified. CellChat analysis revealed that the number and strength of interactions between platelets and a variety of cells increased. We identified key platelet genes from the scRNA-seq data and combined these genes and the results of differential analysis and WGCNA of the bulk RNA-seq data. After univariate Cox regression analysis, we calculated the Cindex of the model constructed by the combination of 90 algorithms, and we finally determined the "CoxBoost + Lasso" combination. Multivariate Cox regression was used to construct the final prognostic model. The qRT-PCR results revealed significant differences in five key prognostic genes between the CLP and sham groups. The data was classified into high- and low-risk groups based on the model score. The high-risk group had a poorer survival rate and less immune infiltration. We identified the importance of platelets in sepsis patients through scRNA-seq, and established prognostic models with key genes that were identified via scRNA-seq combined with bulk RNA-seq analysis. The results of this model were closely associated with patient survival rates and immunological status and this model is useful for the prognostic management of sepsis.