Jingjing Xu,1 Mingyu Zhu,1 Pengxiang Luo,2 Yuanqi Gong1 

1Department of Intensive Care Unit, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China; 2Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China

Correspondence: Yuanqi Gong, Department of Intensive Care Unit, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No. 1 Minde Road, Donghu District, Nanchang, 330006, People’s Republic of China, Email 760225787@qq.com

Background: Sepsis is a syndrome marked by life-threatening organ dysfunction and a disrupted host immune response to infection. PANoptosis is a recent conceptual development, which emphasises the interconnectedness among multiple programmed cell deaths in various diseases. Nevertheless, the role of PANoptosis in sepsis is still unclear.
Methods: We utilized the GSE65682 dataset to identify PANoptosis-related genes (PRGs) and associated immune characteristics in sepsis, classified sepsis samples based on PRGs using the ConsensusClusterPlus method and applied the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify cluster-specific hub genes. Based on PANoptosis -specific DEGs, we compared results from machine learning models and the best-performing model was selected. Predictive efficiency was validated through external dataset, nomogram, survival analysis, quantitative real-time PCR, and western blot.
Results: The expression levels of PRGs were generally dysregulated in sepsis patients compared with normal samples, and higher PRGs expression correlated with increased immune cell infiltration. In addition, two distinct PANoptosis-related clusters were defined, and functional analysis indicated that DEGs associated with these clusters were primarily linked to immune-related pathways. The SVM model was selected as best-performing model, with lower residuals and the highest area under the curve (AUC = 0.967), which was then validated in an external dataset (AUC = 0.989) and through in vivo experiments. Additional validation through nomogram and survival analysis further confirmed its substantial predictive efficacy.
Conclusion: Our findings exposed the intricate association between PANoptosis and sepsis, offering important insights on sepsis diagnosis and potential therapeutic targets.

Keywords: sepsis, PANoptosis, immune infiltration, machine learning, prediction model