ArticleJournal of inflammation research2025
Unveiling PANoptosis in Acute Kidney Injury: An Integrative Multi-Dimensional Approach to Identify Key Biomarkers.
Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- PANoptosis as a drug discovery framework: integrating cell death architecture with clinical translation.Genes and immunity · 2026Review
- Recent advances in PANoptosis research in kidney disease: mechanistic networks, pathological roles, and potential intervention strategies.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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