Yuan Defeng, Zhang Feng, Lv Pengfei, Zhu Jun, Zhang Haiwei, Zhang Zhengcong
The eleventh Ward of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China.
The third ward of Medical Oncology, The First Hospital of Lanzhou University, Lanzhou, 730000, Gansu Province, China.
Sci Rep. 2025 Jun 3;15(1):19390. doi: 10.1038/s41598-025-01335-1.
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.
中性粒细胞胞外诱捕网（NETs）与免疫在肝细胞癌（LIHC）进展中发挥关键作用，但其机制仍不清楚。本研究探讨了NETs相关基因（NETs-RGs）和免疫相关基因（IRGs）作为LIHC预后标志物的潜力。LIHC转录组数据和IRGs从公共数据库中获取，NETs-RGs来自先前的研究。确定与关键模块基因相交的差异表达基因（DEGs），随后进行Cox回归分析和机器学习以确定预后基因。构建并验证了风险预测模型和列线图。进行富集分析、免疫浸润和药物敏感性研究以探索潜在机制。采用逆转录定量PCR（RT-qPCR）验证研究结果。确定了五个预后基因——血红素加氧酶1（HMOX1）、基质金属蛋白酶9（MMP9）、肿瘤坏死因子受体超家族成员4（TNFRSF4）、基质金属蛋白酶12（MMP12）和FMS样酪氨酸激酶3（FLT3）。风险模型和列线图显示出强大的预测能力。基因集富集分析揭示了不同风险组中与视黄醇代谢和细胞色素P450药物代谢相关的通路。免疫浸润分析显示调节性T细胞与骨髓来源的抑制细胞（MDSCs）呈正相关，而MDSCs与这五个基因直接相关。药物敏感性分析确定了74种在风险组之间具有不同敏感性的药物；阿昔替尼在高危患者中显示出较低的敏感性，而ABT-888显示出较高的敏感性。RT-qPCR证实LIHC组织中HMOX1和FLT3表达降低，而MMP9和TNFRSF4上调。本研究建立了一个强大的LIHC预后预测模型，为临床管理和个性化治疗策略提供了有价值的见解。
