Ning Wang,1,* Lina Zhang,1,2,* Ziyu Xu,1,* Qin Xu,1,2 Yanfang Lu,1,2 Peiyuan Niu,1,2 Lei Yan,1,2 Limeng Wang,1,2 Huixia Cao,1,2 Fengmin Shao1,2 

1Department of Nephrology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, People’s Republic of China; 2Henan Provincial Key Laboratory of Kidney Disease and Immunology, Henan Provincial Clinical Research Center for Kidney Disease, Henan Provincial People’s Hospital, Zhengzhou, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Limeng Wang; Fengmin Shao, Department of Nephrology, Zhengzhou University People’s Hospital, Henan Provincial People’s Hospital, Zhengzhou, People’s Republic of China, Email wanglimeng668@126.com; fengminshao@126.com

Background: Programmed cell death and inflammatory responses are critical in the progression of acute kidney injury (AKI). PANoptosis, a highly regulated and complex form of programmed inflammatory cell death, integrates the molecular mechanisms of apoptosis, pyroptosis, and necroptosis. While this process has been implicated in various inflammatory conditions, its specific role in AKI remains unclear.
Methods: The role of PANoptosis in AKI was investigated using single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic data. Initially, scRNA-seq was utilized to identify differentially expressed genes (DEGs) associated with apoptosis, pyroptosis, and necroptosis in individual AKI cells. Through integrating these DEGs, a candidate gene set associated with PANoptosis was established. Several machine learning algorithms were employed to determine the optimal feature genes. The diagnostic potential of these genes was examined through receiver operating characteristic curve analysis. Gene set enrichment analyses were performed to explore their relationship with PANoptosis. Further validation was carried out using AKI animal models.
Results: PANoptosis levels were significantly elevated in AKI. ScRNA-seq revealed heterogeneity in PANoptosis activity across cell types. Integration of transcriptomic data with machine learning algorithms led to the identification of five key upregulated genes: EGR1, CEBPD, HSPA1A, HSPA1B, and RHOB. The diagnostic potential of these genes was demonstrated with the area under curve values of 0.981 for EGR1, 0.920 for CEBPD, 0.968 for HSPA1A, 0.970 for HSPA1B, and 0.953 for RHOB. Functional enrichment analysis demonstrated a significant positive correlation between the expression of these biomarkers and PANoptosis activity. Validation through Western blot and immunohistochemistry further confirmed their roles in AKI pathogenesis.
Conclusion: By integrating scRNA-seq and transcriptomic data, along with the application of innovative methodologies, five key PANoptosis-related genes associated with AKI were identified. Our study offers new insights into the role of PANoptosis in AKI and highlights potential biomarkers for clinical evaluation and therapeutic targeting.

Keywords: acute kidney injury, PANoptosis, single-cell RNA sequencing, machine learning, biomarkers