Cuproptosis-related Gene Signatures and Immunological Characterization in Sepsis-associated Acute Lung Injury - Zhu, Mingyu; Tang, Xiaokai; Xu, Jingjing; Gong, Yuanqi (Combinatorial Chemistry & High Throughput Screening, 2025)
Background Sepsis is a frequent cause of Acute Lung Injury (ALI), characterized by immune dysregulation and a high mortality rate. The role of cuproptosis, a recently discovered cell death mechanism, in sepsis-associated ALI is still unclear. The study aimed to investigate the regulatory mechanisms and immune characteristics associated with cuproptosis in sepsis-associated ALI, with implications for novel diagnostic and therapeutic approaches. Methods Data from the GEO database was utilized to conduct a comprehensive analysis of the cuproptosis-related genes (CRGs) in sepsis-associated ALI. Gene enrichment analysis, WGCNA, CIBERSORT algorithm, and consensus clustering were employed to investigate the associations between CRGs and immune cells. A predictive model for sepsis-associated ALI was developed based on key CRGs, and its diagnostic accuracy was assessed. Finally, qPCR was employed to validate alterations in the expression of CRGs in the sepsis-associated ALI cellular model. Results A total of 14 CRGs were identified in sepsis-associated ALI. Strong correlations between the CRGs and immune cells were observed, and two different CRG subtypes were identified. The expression of immune-related factors in both the CRG and gene clusters exhibited similarities, suggesting a connection between the subgroups and immune cells. The prediction model effectively forecasted the incidence of sepsis-associated ALI based on the expression of CRGs. Finally, qPCR analysis confirmed that the expressions of CRGs in the sepsis-associated ALI cell model closely matched those identified through bioinformatic analyses. Conclusion The study comprehensively evaluated the complex relationship between cuproptosis and sepsis-associated ALI. CRGs were found to be significantly associated with the occurrence, immune characteristics, and biological processes of sepsis-associated ALI. These findings provide valuable new insights into the mechanisms underlying sepsis-associated ALI.
Keywords: bioinformatics