| 基于血管生成拟态相关基因构建胃癌预后预测模型 |
| 投稿时间：2025-04-30 修订日期：2025-06-21 点此下载全文 |
| 引用本文：李文政,李奕言,邢傲雪,张一曼,吴刚,张伟.基于血管生成拟态相关基因构建胃癌预后预测模型[J].医学研究杂志,2025,54(11):88-94 |
| DOI：
10.11969/j.issn.1673-548X.2025.11.017 |
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| 基金项目:河南省医学科技攻关计划项目(LHGJ20230024) |
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| 中文摘要:目的 基于血管生成拟态相关基因(vasculogenic mimicry related gene, VMRG)构建胃癌预后预测模型,为预测胃癌患者生存结局提供新工具。方法 整合癌症基因组图谱(the cancer genome atlas, TCGA)和基因表达数据集(gene expression omnibus,GEO)数据,首先进行差异表达基因分析,后采用LASSO回归及多因素COX回归筛选VMRG并构建胃癌预后预测模型,并通过Western blot法验证独立预后因子的表达水平。通过Kaplan-Meier生存分析、受试者工作特征(receiver operating characteristic,ROC)曲线评估模型性能,并在GSE84433数据集中验证。结合风险评分及临床病理参数的COX回归结果构建列线图及校准曲线,分析模型独立预后价值。然后基于风险评分分层评估高低风险组的肿瘤微环境(tumor microenvironment, TME)、免疫检查点表达(immune checkpoint expression, ICE)差异。结果 建立了基于14个VMRG(如SERPINE1、CTHRC1、NETO2等)的胃癌预后预测模型。多因素COX分析结果显示,NETO2为独立预后因子(HR=1.197,P<0.05),其高表达与患者不良预后显著相关。Western blot法检测结果显示,NETO2蛋白在胃癌细胞系中的表达水平显著高于正常胃上皮细胞。模型在训练集(TCGA-STAD)和验证集(GSE84433)中均表现出良好的预测效能,1、3、5年生存率的曲线下面积(area under the curve, AUC)分别为0.658、0.755、0.701和0.642、0.611、0.607,且Kaplan-Meier生存曲线分析结果均显示高风险组患者生存期显著短于低风险组(P<0.05)。校准曲线分析结果显示,列线图预测生存率与实际值高度一致。TME与ICE结果均表明,与低风险组比较,高风险组患者的免疫评分和免疫检查点相关基因的表达均较高(P<0.05)。结论 本研究基于VMRG构建的胃癌预后预测模型可较好地预测胃癌预后并为免疫治疗提供个体化决策,其中模型中独立因子NETO2在实验验证中与分析结果一致。 |
| 中文关键词:胃癌 血管生成拟态 预后预测模型 免疫浸润 肿瘤微环境 列线图 |
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| Construction of Prognostic Prediction Model for Gastric Cancer Based on Vasculogenic Mimicry Related Gene. |
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| Abstract:Objective To construct a prognostic prediction model for gastric cancer based on vasculogenic mimicry related gene (VMRG), and provide a new tool for predicting survival outcomes of gastric cancer patients. Methods Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were integrated, differentially expressed genes were first identified, followed by screening of VMRG using LASSO regression and multivariate COX regression to construct a prognostic model for gastric cancer. Expression levels of independent prognostic factors were verified by Western blott. Model performance was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve, and validated in the GSE84433dataset. A nomogram and calibration curve were constructed based on COX regression results incorporating risk scores and clinicopathological parameters to assess the independent prognostic value of the model. Differences in tumor microenvironment (TME) and immune checkpoint expression (ICE) between high- and low-risk groups stratified by risk scores were further analyzed. Results A gastric cancer prognosis prediction model was established based on 14 VMRG (such as SERPINE1, CTHRC1, NETO2). The results of multivariate COX analysis showed that NETO2 was an independent prognostic factor (HR=1.197, P<0.05), and its high expression significantly associated with poor prognosis in patient. The results of Western blot assay showed that the expression level of NETO2 protein in gastric cancer cell lines was significantly higher than that in normal gastric epithelial cells. The model demonstrated good predictive efficacy in both training (TCGA-STAD) and validation (GSE84433) sets, with area under the curve (AUC) of 1-, 3-, and 5-year survival rates were 0.658,0.755,0.701 and 0.642,0.611,0.607, respectively. The results of Kaplan-Meier survival curve analysis showed that the survival period of patients in the high-risk group was significantly shorter than that in the low-risk group (P<0.05). The results of the calibration curve analysis show that the survival rate predicted by the nomogram was highly consistent with the actual value. TME and ICE analyses revealed that compared with the low-risk group, the immune scores and the expression of immune checkpoint-related genes of patients in the high-risk group were higher (P<0.05). Conclusion The gastric cancer prognosis prediction model constructed based on VMRG in this study can effectively predict the prognosis of gastric cancer, and provide individualized decision-making insights for immunotherapy. Among them, the independent factor NETO2 in the model is consistent with the analysis results in the experimental verification. |
| keywords:Gastric cancer Vasculogenic mimicry Prognostic prediction model Immune infiltration Tumor microenvironment Nomogram |
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