Identification of PANoptosis related biomarkers to predict hepatic ischemia‒reperfusion injury after liver transplantation

Hepatic ischemia-reperfusion injury (HIRI) is a major complication following liver transplantation. Bioinformatic analysis was performed to elucidate the PANoptosis-related molecular mechanisms underlying HIRI. Comprehensive analysis of bulk and single-cell RNA sequencing data from human liver tissue before and after HIRI was performed. Differential expression analysis, weighted gene coexpression analysis, and protein interaction network analysis were used to identify candidate biomarkers. Multiple machine learning methods were utilized to screen for core biomarkers and construct a diagnostic predictive model. Functional and interaction analyses of the genes were also performed. Cellular clustering and annotation, pseudotemporal trajectory, and intercellular communication analyses of HIRI were conducted. Six PANoptosis-associated genes (CEBPB, HSPA1A, HSPA1B, IRF1, SERPINE1, and TNFAIP3) were identified as HIRI-related biomarkers. These biomarkers are regulated by NF-κB and miRNA-155. A nomogram for HIRI prediction based on these biomarkers was constructed and validated. In addition, the heterogeneity and dynamic changes in macrophage subpopulations during HIRI were revealed, highlighting the roles of Kupffer cells and monocyte-derived macrophages in modulating the hepatic microenvironment. The MIF and VISFATIN signaling pathways play important roles in the interaction between macrophages and other cells. These findings enhance our understanding of the mechanisms of PANoptosis in HIRI and provide a new basis and potential targets for prevention and treatment strategies for HIRI.

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Publication Details

Journal
Scientific Reports
Published
2025-05-02
DOI
https://doi.org/10.1038/s41598-025-99264-6
Citations
5
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
6.77

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article

Identification of PANoptosis related biomarkers to predict hepatic ischemia‒reperfusion injury after liver transplantation

Xin Lü, Ting Zhang, Yao−Zhong Xu, Xinting Sang et al.
5 citations
Scientific Reports
Liver Disease Diagnosis and Treatment
6.77
article

Identification of PANoptosis related biomarkers to predict hepatic ischemia‒reperfusion injury after liver transplantation

Xin Lü, Ting Zhang, Yao−Zhong Xu, Xinting Sang, Junwei Zhang, Zhihong Chen, Lei Zhang, Yaoge Liu
article en
5 citations

Abstract

Hepatic ischemia-reperfusion injury (HIRI) is a major complication following liver transplantation. Bioinformatic analysis was performed to elucidate the PANoptosis-related molecular mechanisms underlying HIRI. Comprehensive analysis of bulk and single-cell RNA sequencing data from human liver tissue before and after HIRI was performed. Differential expression analysis, weighted gene coexpression analysis, and protein interaction network analysis were used to identify candidate biomarkers. Multiple machine learning methods were utilized to screen for core biomarkers and construct a diagnostic predictive model. Functional and interaction analyses of the genes were also performed. Cellular clustering and annotation, pseudotemporal trajectory, and intercellular communication analyses of HIRI were conducted. Six PANoptosis-associated genes (CEBPB, HSPA1A, HSPA1B, IRF1, SERPINE1, and TNFAIP3) were identified as HIRI-related biomarkers. These biomarkers are regulated by NF-κB and miRNA-155. A nomogram for HIRI prediction based on these biomarkers was constructed and validated. In addition, the heterogeneity and dynamic changes in macrophage subpopulations during HIRI were revealed, highlighting the roles of Kupffer cells and monocyte-derived macrophages in modulating the hepatic microenvironment. The MIF and VISFATIN signaling pathways play important roles in the interaction between macrophages and other cells. These findings enhance our understanding of the mechanisms of PANoptosis in HIRI and provide a new basis and potential targets for prevention and treatment strategies for HIRI.

Scientific ReportsVol. 15(1)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking Union Medical College Hospital (CN)
Peking Union Medical College Hospital
Good health and well-being
Openalex Percentile: Top 3%
Liver Disease Diagnosis and Treatment
6.77
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