ArticleInternational journal of genomics2026
Identification of Key Genes in Acute Liver Failure via Analysis of Programmed Cell Death Patterns.
Article in International journal of genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Identification of Key Genes in Acute Liver Failure via Analysis of Programmed Cell Death Patterns.International journal of genomics · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Acute liver failure (ALF) is a severe condition with high mortality, where programmed cell death (PCD) plays a critical yet not fully understood role. This study aimed to identify key PCD-related genes and explore their potential as biomarkers and therapeutic targets in ALF. Methods: Three HBV-ALF microarray datasets (GSE14668, GSE38941, GSE62029) from GEO were integrated and analyzed. Differential expression analysis, protein-protein interaction (PPI) network construction, and functional enrichment were performed. Machine learning algorithms (LASSO and random forest) were used to identify hub genes. Immune infiltration was assessed via CIBERSORT and ssGSEA. Regulatory networks involving miRNAs and transcription factors (TFs) were constructed. Results: A total of 109 differentially expressed PCD genes were identified. TIMP1 and IL18 were consistently selected as hub genes by both CytoHubba and machine learning methods. These genes demonstrated high diagnostic accuracy and prognostic value in ALF. Immune infiltration analysis revealed significant associations with macrophage polarization. Functional enrichment linked TIMP1 and IL18 to immune and metabolic pathways. A miRNA-mRNA-TF regulatory network was constructed, with STAT3 identified as a key upstream regulator. Conclusion: TIMP1 and IL18 are potential diagnostic biomarkers and candidate therapeutic targets for ALF, closely associated with immune infiltration and PCD processes. These findings provide new insights into the molecular mechanisms of ALF and support the development of targeted therapies.
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