基于浸润自然杀伤细胞及EB病毒相关的特征基因构建胃癌预后模型

卓广政; 毛哲; 黄晶; 江春韵; 刘子怡; 韦妍婷; 潘运宝

doi:10.14188/j.1671-8852.2024.0702

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基于浸润自然杀伤细胞及EB病毒相关的特征基因构建胃癌预后模型

肿瘤学研究 | 更新时间：2026-11-06

- 基于浸润自然杀伤细胞及EB病毒相关的特征基因构建胃癌预后模型
- Construction of a prognostic model for gastric cancer based on infiltrated natural killer cells and EB virus⁃associated signature genes
- 武汉大学学报（医学版） 2025年46卷第12期页码：1572-1582
- 作者机构：
  
  1.武汉大学中南医院检验科湖北武汉 430071
  2.武汉大学第二临床学院湖北武汉 430071
- 作者简介：
  
  卓广政，男，2000—，医学硕士生，主要从事肿瘤标志物相关研究，E⁃mail:2022203030043@whu.edu.com
  潘运宝，男，1985—，医学博士，主任技师，副研究员，博士生导师，主要从事肿瘤标志物筛查与功能研究，E⁃mail:panyunbao@qq.com
- 基金信息：
  
  湖北省公共卫生青年拔尖人才项目(WSJKRC2022013)
- DOI：10.14188/j.1671-8852.2024.0702
  中图分类号： R735.2
- 收稿：2024-09-13，
  
  纸质出版：2025-12-25
- 稿件说明：
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卓广政,毛哲,黄晶等.基于浸润自然杀伤细胞及EB病毒相关的特征基因构建胃癌预后模型[J].武汉大学学报（医学版）,2025,46(12):1572-1582.

ZHUO Guangzheng,MAO Zhe,HUANG Jing,et al.Construction of a prognostic model for gastric cancer based on infiltrated natural killer cells and EB virus⁃associated signature genes[J].Medical Journal of Wuhan University,2025,46(12):1572-1582.
卓广政,毛哲,黄晶等.基于浸润自然杀伤细胞及EB病毒相关的特征基因构建胃癌预后模型[J].武汉大学学报（医学版）,2025,46(12):1572-1582. DOI： 10.14188/j.1671-8852.2024.0702.

ZHUO Guangzheng,MAO Zhe,HUANG Jing,et al.Construction of a prognostic model for gastric cancer based on infiltrated natural killer cells and EB virus⁃associated signature genes[J].Medical Journal of Wuhan University,2025,46(12):1572-1582. DOI： 10.14188/j.1671-8852.2024.0702.

摘要

目的

分析EB病毒（EBV）相关胃癌（EBVaGC）的免疫微环境并识别关键预后标志物，为了解与EBVaGC预后相关的分子靶点提供见解。

方法

利用GEO数据库中的胃癌单细胞RNA测序数据、转录组测序数据及临床信息，结合差异分析、ssGSEA免疫浸润分析、单因素Cox回归分析，筛选与自然杀伤（NK）细胞和EBV相关的胃癌预后特征基因。通过Lasso回归构建风险预后模型，使用Kaplan⁃Meier生存曲线评估模型。使用GSEA富集分析、卡方检验、肿瘤突变负荷分析等方法检验高风险和低风险患者组之间的生物学差异，识别了不同风险和EBV状态亚组中免疫细胞浸润的特定模式、生存差异和通路富集情况。

结果

识别了91个与自然杀伤（NK）细胞和EBV相关的胃癌预后特征基因。基于此构建了包含10个关键基因的预后风险模型并计算了风险评分。该模型的风险评分被证实为预后的独立预测因子。对患者进行风险分层后发现，高风险组生存期显著短于低风险组（

0.001）。低风险组样本中免疫检查点抑制剂（ICIs）治疗的关键靶点基因表达水平显著高于高风险组，预示着低风险组可能更能从免疫治疗中获益。

结论

本研究基于免疫微环境中NK细胞浸润及EBV感染相关特征基因成功构建了胃癌的预后风险模型。该模型不仅能有效评估胃癌患者的生存预后，还能为免疫治疗提供参考。

Abstract

Objective

To analyze the immune microenvironment of Epstein⁃Barr (EB) virus⁃associated gastric cancer (EBVaGC) and identify key prognostic biomarkers

providing insights into molecular targets related to EBVaGC prognosis.

Methods

Single⁃cell RNA sequencing

transcriptome sequencing

and clinical information from gastric cancer datasets in the GEO database were utilized. Combining differential expression analysis

single⁃sample gene set enrichment analysis (ssGSEA) of immune infiltration

and univariate Cox regression analysis

prognostic feature genes related to natural killer (NK) cells and EBVaGC were screened. A prognostic risk model was constructed using Lasso regression

and the model was evaluated with Kaplan⁃Meier survival curves. Biological differences between high⁃risk and low⁃risk patient groups were examined using methods such as GSEA enrichment analysis

chi⁃square tests

and tumor mutation burden analysis. Specific patterns of immune cell infiltration

survival differences

and pathway enrichment were identified across different risk and EB virus status subgroups.

Results

A total of 91 prognostic feature genes associated with NK cells and EBVaGC were identified. Based on these genes

a prognostic risk model was constructed

consisting of 10 key genes

and a risk score was calculated. The risk score was confirmed as an independent prognostic factor. Risk stratification revealed that patients in the high⁃risk group had significantly shorter survival times than those in the low⁃risk group (

0.001). Key target genes for immune checkpoint inhibitor (ICI) therapy were expressed at significantly higher levels in the low⁃risk group

suggesting that those patients may benefit more from immunotherapy.

Conclusion

This study successfully constructed a prognostic risk model for gastric cancer based on EB virus⁃related feature genes and NK cell⁃related feature genes within the immune microenvironment. The model not only effectively evaluates the survival prognosis of gastric cancer patients but also serves as a reference for immunotherapy.

关键词

Keywords

references

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