Abstract
Introduction
: Sepsis is the leading cause of death in critically ill patients resulting in multi-organ dysfunction, including acute respiratory distress syndrome (ARDS). Our study was conducted to determin the role of cellular senescence genes and Immune Infiltration in sepsis and sepsis-induced ARDS using bioinformatics analyses.
Experimental Procedures
: The GSE66890 and GSE145227 datasets were obtained from the Gene Expression Omnibus (GEO) database and utilized for bioinformatics analyses. Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs was performed to identify the key functional modules. Two machine learning algorithms, least absolute shrinkage, and selection operator (LASSO) and support vector machine–recursive feature elimination (SVM-RFE) were utilized for screening characteristic genes among sepsis and sepsis-induced ARDS. ROC curves were generated to evaluate the prediction ability of hub genes. The difference of immune infiltration level between disease and control groups was compared via ssGSEA. The diagnostic value of hub genes were verified using quantitative PCR (qPCR) in our hospital patients.
Results
Four characteristic genes (ATM, CCNB1, CCNA1, and E2F2) were identifified as the biomarker involved in the progression of sepsis-induced ARDS. And E2F2 has the highest prediction ability to predict the occurrence of ARDS from sepsis patients. CD56bright tural killer cell and Plasmacytoid dendritic cell were highly infiltrated in sepsis-induced ARDS group while Eosinophil, MDSC, Macrophage, and Neutrophil was lowly infiltrated. In addition, lower expression levels of ATM gene were observed in sepsis patients than non- sepsis patients (n = 6).
Conclusion
Sepsis-induced ARDS was correlated with circulating immune responses, and the expression of ATM, CCNB1, CCNA1, and E2F2 might be potential diagnostic biomarkers as well as therapeutic target in sepsis-induced ARDS.