头颈鳞状细胞癌自噬相关基因预后风险评分模型构建及验证

陈思霖; 李谦; 王刘倩; 张已; 周雪琴; 张焱; 邓安春

doi:10.16016/j.2097-0927.202207110

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头颈鳞状细胞癌自噬相关基因预后风险评分模型构建及验证

临床医学 | 更新时间：2026-01-09

- 头颈鳞状细胞癌自噬相关基因预后风险评分模型构建及验证
- Construction and validation of prognostic risk score model based on autophagy-related genes for head and neck squamous cell carcinoma
- 陆军军医大学学报 2023年45卷第4期页码：326-334
- 作者机构：
  
  400037 重庆，陆军军医大学(第三军医大学)第二附属医院耳鼻咽喉头颈外科
- 作者简介：
  
  [ 邓安春，E-mail：260019300@qq.com DENG Anchun, E-mail: 260019300@qq.com
- 基金信息：
  
  电磁辐射医学防护教育部重点实验室开放课题(2020DCKF004)
- DOI：10.16016/j.2097-0927.202207110
  中图分类号： R319;R730.7;R739.91
- 收稿：2022-07-16，
  
  修回：2022-10-13，
  
  纸质出版：2023-02-28
- 稿件说明：
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陈思霖, 李谦, 王刘倩, 等. 头颈鳞状细胞癌自噬相关基因预后风险评分模型构建及验证[J]. 陆军军医大学学报, 2023,45(4):326-334.

Silin CHEN, Qian LI, Liuqian WANG, et al. Construction and validation of prognostic risk score model based on autophagy-related genes for head and neck squamous cell carcinoma[J]. Journal of Army Medical University, 2023, 45(4): 326-334.
陈思霖, 李谦, 王刘倩, 等. 头颈鳞状细胞癌自噬相关基因预后风险评分模型构建及验证[J]. 陆军军医大学学报, 2023,45(4):326-334. DOI： 10.16016/j.2097-0927.202207110.

Silin CHEN, Qian LI, Liuqian WANG, et al. Construction and validation of prognostic risk score model based on autophagy-related genes for head and neck squamous cell carcinoma[J]. Journal of Army Medical University, 2023, 45(4): 326-334. DOI： 10.16016/j.2097-0927.202207110.

摘要

目的

构建并验证一种头颈鳞状细胞癌(head and neck squamous cell carcinoma，HNSCC)自噬相关基因预后风险评分模型。

方法

从癌症基因组图谱(The Cancer Genome Atlas，TCGA)数据库下载全部HNSCC转录组表达数据(RNA sequencing，RNA-seq)及临床信息，筛选出差异表达基因，与GeneCards数据库检索的自噬相关基因(autophagy related genes，ARGs)取交集，得到差异表达的ARGs，整合临床信息后经预后分析获得预后相关的ARGs，再对其富集分析。应用套索(the least absolute shrinkage and selection operator，LASSO)回归和Cox回归模型构建一种用于预测HNSCC预后及生存情况的风险评分模型；绘制受试者工作特征(receiver operating characteristic

ROC)曲线，计算曲线下面积(area under the curve

AUC)及最佳截断(cut-off)值，并以最佳cut-off值将患者分为高、低风险评分组；绘制Kaplan-Meier生存曲线评价该模型的预测性能；将临床信息与风险评分对应整合，再次利用Cox回归分析评价风险评分的独立预后价值。

结果

通过对差异表达的ARGs进行预后分析初筛出20个与预后相关的ARGs，再应用LASSO回归和Cox回归分析获得9个与预后显著相关的ARGs，以此构建HNSCC的预后风险评分模型。ROC曲线及Kaplan-Meier生存曲线分析显示，低风险评分组生存时间优于高风险评分组，两组生存时间差异有统计学意义(

0.001)，且该模型在训练集(AUC最大值为0.69)与外部验证集(AUC最大值为0.822)中均展现出良好的预测性能。纳入临床信息后，对风险评分进行Cox回归分析，提示其与HNSCC患者预后显著相关(

0.001)，表明风险评分对HNSCC具有独立预后价值。

结论

由9个ARGs组成的HNSCC风险评分模型，可有效预测HNSCC患者的预后情况。

Abstract

Objective

To construct and validate a prognostic risk-scoring model based on autophagy-related genes for head and neck squamous cell carcinoma (HNSCC).

Methods

All HNSCC transcriptome expression data (RNA sequencing

RNA-seq) and clinical information downloaded from the Cancer Genome Atlas (TCGA) database

and differentially expressed genes were screened. These differentially expressed genes of HNSCC were intersected with autophagy related genes (ARGs) retrieved from the GeneCards database to obtain differentially expressed ARGs. After integrating clinical information

prognostic ARGs were obtained by prognostic analysis

and then enrichment analysis was performed. The least absolute shrinkage and selection operator (LASSO) regression and Cox regression model were used to construct a risk scoring model for predicting the prognosis and survival of HNSCC. The receiver operating characteristic (ROC) curve was drawn

the area under the curve (AUC) and the best cut-off value were calculated

and the patients were divided into the high- and low-risk score groups with the best cut-off value. Kaplan-Meier survival curve was drawn to assess the predictive performance of the model. The clinical information was integrated with the risk score

and the independent prognostic value of the risk score was evaluated by Cox regression analysis.

Results

The prognostic risk score model of HNSCC was constructed based on the 9 ARGs significantly related to prognosis were obtained by LASSO regression and Cox regression analysis through the prognostic analysis for differentially expressed ARGs which screened 20 ARGs related to prognosis. The survival time of the low-risk score group was better than that of the high risk score group

and the survival time of the two groups was significantly different (

0.001)

according to ROC curve and Kaplan-Meier survival curve. The model showed good prediction performance in both the training set (the maximum AUC

0.69) and the external validation set (the maximum AUC

0.822). Cox regression analysis showed that the risk score was significantly correlated with the prognosis of HNSCC patients (

0.001)

indicating that the risk score had independent prognostic value for HNSCC.

Conclusion

The HNSCC risk scoring model composed of 9 ARGs can effectively predict the prognosis of patients with HNSCC.

关键词

Keywords

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