Abstract
Background: Sepsis is an infection-induced dysregulated cellular response that leads to multiorgan dysfunction. As a time-sensitive condition, sepsis requires prompt diagnosis and standardized treatment. This study investigated the impact of biomarkers identified in peripheral whole blood from sepsis patients (24-h post-onset) on sepsis-induced acute lung injury (ALI) using bioinformatics and machine learning approaches. Methods: Gene Expression Omnibus (GEO) datasets were analyzed for functional and differential gene expression. Critical genetic markers were identified and evaluated using multiple machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) and cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) were conducted to explore associations between biomarkers and immune cells. Biomarker expression was further validated through animal experiments. Result: A total of 611 overlapping differentially expressed genes (DEGs) were identified in GSE54514, including 361 upregulated and 250 downregulated genes. From GSE95233, 1150 DEGs were detected, with 703 upregulated and 447 downregulated genes. Enrichment analysis revealed DEGs associated with immune cell activity, immune cell activation, and inflammatory signaling pathways. Component 3a receptor 1 (C3AR1) and secretory leukocyte peptidase inhibitor (SLPI) were identified as critical biomarkers through multiple machine learning approaches. CIBERSORT analysis revealed significant associations between immune cell types and C3AR1/SLPI. Moreover, the scRNA-seq analysis demonstrated that the SLPI expression was significantly elevated in immunological organ cells during the early stages of sepsis, a finding further validated in sepsis-induced ALI models. Conclusion: This study employed machine learning techniques to identify sepsis-associated genes and confirmed the importance of SLPI as a biomarker within 24 h of sepsis onset. SLPI also played a significant role in sepsis-induced ALI, suggesting its potential as a novel target for personalized medical interventions, targeted prevention, and patient screening.