# Investigation of the Significance of Blood Signatures on Sepsis-Induced Acute Lung Injury in Sepsis Within 24 Hours

## TL;DR

This study identifies blood biomarkers linked to sepsis-induced lung injury within 24 hours using machine learning and genetic analysis.

## Contribution

The study introduces SLPI as a novel biomarker for early sepsis detection and lung injury prediction.

## Key findings

- 611 and 1150 differentially expressed genes were identified in two datasets, linked to immune and inflammatory pathways.
- SLPI and C3AR1 were identified as critical biomarkers associated with immune cell activity in sepsis.
- SLPI expression was elevated in immune cells during early sepsis and validated in animal models of lung injury.

## 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.