ArticleScientific reports2025
A reliable prognostic model for hepatocellular carcinoma using neutrophil extracellular traps and immune related genes.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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
2 citing papers in PubMed.
- The Molecular Network of Neutrophil Extracellular Traps in Hepatocellular Carcinoma: Biogenesis, Function, and Therapeutic Implications.Molecules (Basel, Switzerland) · 2026Review
- Neutrophils in the hepatocellular carcinoma microenvironment: orchestrators of progression and immunity.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Neutrophil extracellular traps (NETs) and immunity play critical roles in liver hepatocellular carcinoma (LIHC) progression, but their mechanisms remain unclear. This study explored the potential of NETs-related genes (NETs-RGs) and immune-related genes (IRGs) as prognostic markers for LIHC. LIHC transcriptome data and IRGs were obtained from public databases, and NETs-RGs were derived from prior research. Differentially expressed genes (DEGs) intersecting with key module genes were identified, followed by Cox regression analysis and machine learning to determine prognostic genes. A risk prediction model and nomogram were constructed and validated. Enrichment analysis, immune infiltration, and drug sensitivity studies were conducted to explore underlying mechanisms. Reverse transcription quantitative PCR (RT-qPCR) was used to validate findings. Five prognostic genes-HMOX1, MMP9, TNFRSF4, MMP12, and FLT3-were identified. A risk model and nomogram demonstrated strong predictive ability. Gene set enrichment analysis revealed pathways related to retinol metabolism and cytochrome P450 drug metabolism in different risk groups. Immune infiltration analysis showed regulatory T cells positively correlated with MDSCs, which were directly associated with the five genes. Drug sensitivity analysis identified 74 drugs with differential sensitivity between risk groups; axitinib showed lower sensitivity in high-risk patients, while ABT-888 showed higher sensitivity. RT-qPCR confirmed reduced HMOX1 and FLT3 expression in LIHC tissues, while MMP9 and TNFRSF4 were upregulated. This study developed a robust predictive model for LIHC prognosis, offering valuable insights for clinical management and personalized treatment strategies.
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