LILRA5

Background: In sepsis, oxidative stress (OS) triggers essential adaptive responses and emerging OS-related biomarkers show potential for enhancing sepsis diagnosis and therapy. Methodology: In this study, we used single-cell datasets and the OS gene set to identify immune cell types with the highest oxidative activity across different sepsis states. Differential expression genes (DEG) between "high state" cells and "low state" cells were screened. High-dimensional weighted gene co-expression network analysis (hdWGCNA), combined with multiple machine learning methods, was used for the selection of hub genes. Expressions of hub genes were then validated. Cell-cell communication and transcription factor analysis were performed later. Real-time quantitative reverse transcription (qRT-PCR) and Western blotting validated expression of LILRA5 in both the cecal ligation and puncture (CLP) model and the lipopolysaccharide-induced sepsis model. Reactive oxygen species (ROS) levels were also detected in THP-1 cells after silencing LILRA5. Results: In the early stages of sepsis, oxidative activity reaches its peak, with macrophages displaying the highest OS among all cell types. Through the application of the "Quartile method", all cells were clustered into three states based on OS activity (low, medium, and high). LILRA5, MGST1, PLBD1, and S100A9 were selected as hub genes and significantly upregulated in sepsis. LILRA5 was predominantly expressed in macrophages and was highly expressed in the early stage of macrophage. Specifically, LILRA5 Conclusion: In conclusion, our study has mapped the landscape of OS dynamics in sepsis and found that LILRA5