医学分子生物学杂志 ›› 2025, Vol. 22 ›› Issue (1): 76-83.doi: 10.3870/j.issn.1672-8009.2025.01.012

• 论著 • 上一篇    下一篇

基于生物信息学的胃癌关键糖酵解相关差异表达基因的鉴定及预后模型的构建 #br#

  

  1. 仙桃市第一人民医院1肿瘤科,2胃肠外科 湖北省仙桃市, 433000
  • 出版日期:2025-01-31 发布日期:2025-02-28

Identification of Differentially Expressed Glycolysis-related Genes and Establishment of Prognostic Model for Gastric Cancer by Bioinformatics #br#

  1. 1Department of Oncology,2Department of Gastrointestinal Surgery, Xiantao First Peoples Hospital, Xiantao, Hubei, 43300, China
  • Online:2025-01-31 Published:2025-02-28

摘要: 目的 应用生物信息学工具探索糖酵解相关基因 (glycolysis-related genes, GRGs) 在胃癌中的表达及基于其所构建的风险评分模型与胃癌患者预后的关系方法 从癌症基因组图谱 (the cancer genome atlas, TCGA) 数据库下载胃癌组织转录组和临床特征数据从基因集富集分析 (gene set enrichment analysis, GSEA) 数据库中获得 GRGs 使用 limma 包来识别胃癌组织中差异表达的 GRGs。 使用单因素 Cox 回归分析筛选预后相关的 GRGs, 应用 LASSO 回归分析构建基于 GRGs 的预后预测模型根据 Cox 回归分析确定的独立预后危险因素构建 Nomogram 最后, 分析22 种浸润性免疫细胞与 GRGs、 风险评分模型相关性结果 鉴定出21 个差异表达的 GRGs, 其主要富集在酒精代谢过程和酪氨酸代谢途径利用 LASSO Cox 模型筛选出5 个预后相关的糖酵解基因 (ADH1BADH4、 CLDN9、 VCAN LHX90), 构建预测胃癌预后风险评分模型低风险评分的胃癌患者总生存时间明显优于高风险评分的胃癌患者, ROC 曲线分析显示该模型预测胃癌患者 1 、 3 年及 5 年生存期曲线下面积 (area under curve, AUC) 值分别为0. 602、 0. 680 0. 802, 发现该模型预测患者生存期的效能优于肿瘤分期及分级单因素和多因素 Cox 分析显示构建的模型患者的年龄性别及肿瘤分期分级是胃癌患者独立预后因素基于独立危险因素构建了用来预测胃癌患者生存 Nomogram 最后, 发现 CD8 T 细胞和辅助性滤泡T 细胞比例在高危组中明显降低, M0 型和 M2 型巨噬细胞比例在高危组中明显高于低危组辅助性滤泡 T 细胞与 ADH1B 表达水平VCAN 表达水平和风险评分呈负相关。 M2 型巨噬细胞与 VCAN 表达水平和风险评分呈正相关结论 研究筛选出胃癌预后相关的5 GRGs 构建的模型可作为评估胃癌患者预后的可靠工具, 为胃癌提供潜在的糖酵解治疗靶点

关键词: 胃癌, 糖酵解, 差异表达基因, Cox 回归, 预后指标, 诺莫图

Abstract: Objective To explore the expression of glycolysis-related genes ( GRGs) in gastric cancer by bioinformatics, and the relationship between the established risk scoring model andprognosis of gastric cancer. Methods The genes expression profiles and clinical feature data of gastric cancer samples were downloaded from the Cancer Genome Atlas (TCGA) database. The GRGs set was obtained from the GSEA database. We used “ limma” packets to identify differentially expressed GRGs in gastric cancer tissues, and used univariate Cox regression analysis to screen for prognosis related GRGs. Then, LASSO regression analysis was used to construct a prognosis prediction model based on the GRGs. A nomogram was developed based on the independent prognostic risk factors determined by Cox regression analysis. Finally, the correlations between 22 types of infiltrating immune cells and the GRGs or risk scoring models were analyzed. Results Twenty-one differentially expressed GRGs were identified, which were mainly enriched in the alcohol metabolism andtyrosine metabolism pathways. Finally, 5 prognostic related glycolytic genes ( ADH1B, ADH4, CLDN9, VCAN, and LHX90) were selected by LASSO and Cox models and were used to constructa gene signature for gastric cancer prognosis prediction. The overall survival of gastric cancer patients with low-risk scores is significantly better than that of gastric cancer patients with high-risk scores. The ROC curve analysis showed that the values of area under the curve (AUC) of the abovemodel to predict the 1-year, 3-year, and 5-year survival for the gastric cancer patients were 0. 602,0. 680, and 0. 802, respectively. The effectiveness of this model to predict the survival of gastriccancer patients was better than that of using tumor staging and grading. Univariate and multivariate Coxanalysis showed that the prognostic model, age, gender, tumor stage and tumor grade were independent prognostic factors for gastric cancer. Based on these prognostic factors, a Nomogram was constructed to predict the survival of gastric cancer patients. Finally, we found that the proportion of CD8T cells and helper follicular T cells was significantly reduced in the high-risk group, while the proportion of M0 macrophages and M2 macrophages was significantly higher in the high-risk group than inthe low-risk group. The proportion of helper follicular T cells was negatively correlated with the expression levels of ADH1B and VCAN and the risk scores. The proportion of M2 macrophages was positivelycorrelated with the expression level of VCAN and the risk scores. Conclusion The 5 GRGs screenedout in this study are related to the prognosis of gastric cancer, which can be used for the prognosis ofgastric cancer patients and may be used as potential therapeutic targets for gastric cancer.

Key words:

gastric cancer, glycolysis, differential gene expression, Cox regression, prognostic marker, nomogram

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