AI Insight
This study identified four cell cycle-related genes (UPP1, DRAM1, GADD45A, and MAPK14) as potential diagnostic biomarkers for sepsis through integrated analysis of bulk and single-cell RNA sequencing data. All four biomarkers showed high diagnostic accuracy (AUC > 0.9) and were significantly upregulated in sepsis patients compared to controls. Single-cell analysis revealed that these biomarkers are primarily expressed in CD16+ and CD14+ monocytes and their expression increases during monocyte differentiation, suggesting a role in sepsis-associated immune dysfunction.
Why it matters
These biomarkers could improve early sepsis diagnosis, which is critical given sepsis's high mortality rate and the importance of rapid treatment initiation. The findings also highlight potential therapeutic targets and identified 19 candidate drugs that may be repurposed for sepsis treatment, though experimental validation is needed.
Understand the Science
by Mei-Ping Zheng, Yan-Ling Du, Xiong-Bin Liao, Ming-Quan Qiu, Huatian Luo, Xiao-Tan Gao
Background
Sepsis is a life-threatening organ dysfunction arising from a dysregulated host response to infection. Cell-cycle disturbance is increasingly recognized as a driver of sepsis-associated immune dysfunction. This study aimed to identify cell cycle-associated diagnostic biomarkers and clarify their roles in sepsis.
Methods
Transcriptomic profiles from the Gene Expression Omnibus (GEO) database were analyzed to identify candidate genes by overlapping differentially expressed genes (DEGs) between sepsis and control samples with cell cycle-related genes (CCRGs). Biomarkers were subsequently screened via machine learning algorithms, followed by expression level validation and receiver operating characteristic (ROC) curve analysis. Furthermore, gene set enrichment analysis (GSEA), immune infiltration analysis, and drug prediction were performed.Finally, single-cell RNA sequencing (scRNA-seq) data were integrated for cell annotation and biomarker expression analysis, enabling the identification of key cells and the reconstruction of pseudotime trajectories.
Results
UPP1, DRAM1, GADD45A, and MAPK14 were selected as biomarkers and were significantly upregulated in sepsis samples (area under the curve (AUC) > 0.9). Additionally, GSEA revealed 65 pathways that were shared across all biomarkers, such as toll-like receptor signaling and antigen processing and presentation. Immune analysis revealed altered infiltration of 14 cell subsets in sepsis, including increased neutrophil numbers and decreased CD8+ T cell numbers. Drug prediction analysis identified 19 potential drugs, including doxorubicin hydrochloride and cisplatin with dual-targeting capacity. Finally, scRNA-seq confirmed CD16+ and CD14+ monocytes as key cells among the six cell types, with all biomarkers showing increasing expression trends during their differentiation.
Conclusion
This study identified four cell cycle-associated biomarkers for sepsis and provided computational evidence linking them to sepsis-related pathways and monocyte differentiation. These findings may provide useful clues for future experimental validation and biomarker development.