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基于糖酵解基因头颈部鳞状细胞癌预后风险模型的构建
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Abstract:
目的：基于糖酵解基因构建头颈部鳞状细胞癌的预后风险模型。方法：从TCGA数据库中下载头颈部鳞状
细胞癌的转录组数据和临床信息。从GSEA官网中下载糖酵解相关基因集，并进行基因集富集分析，筛选
出表达有显著差异的糖酵解基因集进行后续分析。使用单因素COX回归分析、多因素COX回归分析、
LASSO回归分析构建头颈部鳞状细胞癌的预后风险模型。将风险评分与年龄、性别、肿瘤分期、分级等
临床特征结合，构建动态列线图并绘制校准曲线。使用cbioportal进行模型基因突变情况分析和模型基
因的差异分析。用Kaplan-Meier法进行高风险组和低风险组总生存期差异分析和数据分层分析。结果：
成功构建出基于16个糖酵解基因的头颈部鳞状细胞癌的预后风险模型，并且可以作为独立预测因子预测
患者的预后。结论：基于16个糖酵解基因构建出的HNSCC的预后风险模型，为HNSCC的诊断、治疗以及
预后提供新的靶点和方向。
Objective: To construct a prognostic risk model for head and neck squamous cell carcinoma based
on glycolysis-related genes. Methods: The transcriptome data and clinical information of head and
neck squamous cell carcinoma were downloaded from TCGA database. Glycolytic gene sets were
downloaded from the official website of GSEA, and gene set enrichment analysis was conducted to
screen out glycolytic gene sets with significant differences in expression for subsequent analysis.
Univariate COX regression analysis, multivariate COX regression analysis and LASSO regression
analysis were used to construct the prognostic risk model of head and neck squamous cell carcinoma.
The risk score was combined with clinical characteristics such as age, gender, tumor stage
and grade to construct a dynamic nomogram and draw a calibration curve. Cbioportal was used to
analyze the mutation status of model genes and the difference of model genes. Kaplan-Meier method
was used to analyze the difference of overall survival between the high-risk group and the
low-risk group and to analyze the data stratification. Results: The prognostic risk model of head
and neck squamous cell carcinoma based on 16 glycolysis-related genes was successfully constructed
and could be used as an independent predictor to predict the prognosis of patients. Conclusion:
The prognostic risk model of HNSCC based on 16 glycolysis-related genes provides a new target
and direction for the diagnosis, treatment and prognosis of HNSCC.
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