杜也, 米热阿依·阿布都热孜克, 左冉, 袁东琪, 霍庚崴, 陈金良, 张翠翠, 孟昭婷, 陈鹏. 基于LASSO回归筛选影响肺腺癌患者预后的糖酵解相关基因[J]. 中国肿瘤临床, 2023, 50(1): 16-21. DOI: 10.12354/j.issn.1000-8179.2023.20220189
引用本文: 杜也, 米热阿依·阿布都热孜克, 左冉, 袁东琪, 霍庚崴, 陈金良, 张翠翠, 孟昭婷, 陈鹏. 基于LASSO回归筛选影响肺腺癌患者预后的糖酵解相关基因[J]. 中国肿瘤临床, 2023, 50(1): 16-21. DOI: 10.12354/j.issn.1000-8179.2023.20220189
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Ye Du, Abdurazik Mihray, Ran Zuo, Dongqi Yuan, Gengwei Huo, Jinliang Chen, Cuicui Zhang, Zhaoting Meng, Peng Chen. Screening of glycolysis-related genes affecting prognosis of patients with lung adenocarcinoma based on LASSO regression[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(1): 16-21. DOI: 10.12354/j.issn.1000-8179.2023.20220189
Citation: Ye Du, Abdurazik Mihray, Ran Zuo, Dongqi Yuan, Gengwei Huo, Jinliang Chen, Cuicui Zhang, Zhaoting Meng, Peng Chen. Screening of glycolysis-related genes affecting prognosis of patients with lung adenocarcinoma based on LASSO regression[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2023, 50(1): 16-21. DOI: 10.12354/j.issn.1000-8179.2023.20220189
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基于LASSO回归筛选影响肺腺癌患者预后的糖酵解相关基因

Screening of glycolysis-related genes affecting prognosis of patients with lung adenocarcinoma based on LASSO regression

    摘要
  • 摘要:
      目的  确定用于评估肺腺癌（lung adenocarcinoma，LUAD）患者糖酵解相关基因的风险评分模型。
      方法  使用公共数据库癌症基因组图谱（The Cancer Genome Atlas，TCGA）中LUAD患者转录组数据，通过基因富集分析（gene set enrichment analysis，GSEA）、差异表达基因（differentially expressed genes，DEGs）分析和最小绝对收缩选择算子（least absolute shrinkage and selection operator，LASSO）回归分析构建风险预测模型。通过Kaplan-Meier分析、受试者工作特征（receiver operating characteristic，ROC）曲线、单因素及多因素Cox回归分析验证模型预测性能。使用CIBERSORT算法计算高、低风险两组免疫细胞浸润差异。构建用于临床预测患者预后的列线图。
      结果  识别出3个糖酵解相关基因集，筛选出6个糖酵解相关基因构建风险评分模型。高风险组总生存率显著低于低风险组，验证性结果显示该模型有良好的预测性能。高、低风险两组的免疫细胞浸润情况存在显著差异。列线图的构建开发了一种可以预测LUAD患者生存率的定量方法。
      结论  基于糖酵解相关基因构建的风险评分模型为早期LUAD患者预测预后提供了新型生物标志物。

     

    Abstract:
      Objective  To identify risk scoring models for assessing glycolysis-related genes in patients with lung adenocarcinoma (LUAD).
      Methods  A risk prediction model was constructed through gene set enrichment analysis (GSEA), differentially expressed genes (DEGs) analysis, and least absolute shrinkage and selection operator (LASSO) regression using The Cancer Genome Atlas (TCGA) public database for patients with LUAD transcriptomic data. The model prediction performance was further validated using Kaplan–Meier analysis, receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox analyses. The CIBERSORT algorithm was used to calculate differences in immune cell infiltration between the high- and low-risk groups. Finally, a nomogram was constructed for clinical predictions of patient prognosis.
      Results  Three glycolysis-related gene sets were identified, and six glycolysis-related genes were needed to construct a risk score model. The overall survival rate was significantly lower in the high-risk group than in the low-risk group. The validation results revealed that the model had good predictive performance. Significant differences in immune cell infiltration were noted between the two groups. The constructed column line graphs developed a quantitative method that could predict the survival of patients with LUAD.
      Conclusions   The risk score models established based on glycolysis-related genes provide novel biomarkers for predicting the prognosis of patients with early stage LUAD.

     

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