Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5

Abstract

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

Keywords: LILRA5; machine learning; molecular docking; sepsis; single cell.