基于自噬相关基因建立和验证口腔鳞状细胞癌的预后风险评分模型

doi:10.3969/j.issn.1005-4979.2022.06.003

摘要/Abstract

摘要：

目的: 构建自噬相关基因(autophagy-related genes,ARGs)的预后风险评分模型,预测口腔鳞状细胞癌(oral squamous cell carcinoma,OSCC)的生存率。方法: 从癌症基因组图谱(the cancer genome atlas,TCGA)数据库中检索差异表达的ARGs,通过 Cox 回归分析及套索(the least absolute shrinkage and selection operator, LASSO)回归分析确定预后的ARGs,构建总生存期(overall survival, OS)风险评分模型。根据中位风险评分将TCGA数据库和基因表达汇编(gene expression omnibu,GEO)数据库中所有病例归类为低风险组和高风险组,使用Kaplan-Meier(K-M)法分析高、低风险组的OS,通过描述高、低风险组患者风险评分分布、生存状态分布和基因表达模式论证风险评分模型的准确性。构建列线图定量估计OSCC 患者的生存率。通过免疫细胞浸润差异分析探讨该模型预测OSCC预后的潜在机制。结果: 筛选出37个差异表达的ARGs,构建了由6个预后ARGs(BID、DDIT3、VEGFA、FADD、BIRC5和NKX2-3)组成的OSCC预后风险评分模型,该模型可作为OSCC潜在的独立预后因素。构建了列线图预测患者OS,校准曲线以图形方式验证了实际生存率与列线图预测的生存率之间的一致性。结论: 本研究构建的风险评分模型和列线图可以为预测OSCC患者预后提供参考,并且这有助于理解自噬影响OSCC发生、发展的机制。

关键词: 口腔鳞状细胞癌, 自噬, 预后, 风险评分, 列线图

Abstract:

Objective: The research focused on constructing the prognostic risk score model of autophagy-related genes(ARGs) and predicting survival rate of oral squamous cell carcinoma(OSCC). Methods: Differentially expressed ARGs were retrieved from the cancer genome atlas(TCGA) database. Prognostic ARGs were identified through Cox regression and the least absolute shrinkage and selection operator(LASSO) regression analysis to construct the overall survival(OS) risk score model. Next, all cases in TCGA and gene expression omnibu (GEO) datasets were classified into low-risk or high-risk groups based on the median risk score. Kaplan-Meier(K-M) method was used to analyze the OS of high- and low-risk groups. In addition, the risk distribution, survival status and gene expression patterns were analyzed to demonstrate the accuracy of the risk score model. A nomogram was constructed to quantitatively estimate the survival rate of OSCC patients. Finally, differential immune cell infiltration analysis was conducted to explore the potential mechanism of the model predicting the prognosis of OSCC. Results: A total of 37 differentially expressed ARGs were screened out. 6 ARGs (BID、DDIT3、VEGFA、FADD、BIRC5、NKX2-3) were used to construct a prognostic risk score model of OSCC, which could be considered as a potentially independent prognostic factor of OSCC. The calibration curve from nomogram chart graphically verified the consistency between the actual and predicted survival rates. Conclusions: The authors suggest that the autophagy-related risk score model and the nomogram constructed in this study can provide a reference for predicting the prognosis of OSCC patients, which will be helpful to understand the underlying mechanism of autophagy participating the occurrence and development of OSCC.

Key words: oral squamous cell carcinoma, autophagy, prognosis, risk score, nomogram

中图分类号:

R782
R739.8

齐延旭, 马川, 朱勇, 黄晟栋, 赵华强, 张风河. 基于自噬相关基因建立和验证口腔鳞状细胞癌的预后风险评分模型[J]. 《口腔颌面外科杂志》, 2022, 32(6): 343-353.

QI Yanxu, MA Chuan, ZHU Yong, HUANG Shengdong, ZHAO Huaqiang, ZHANG Fenghe. Construction and validation of prognostic risk score model for OSCC based on autophagy-related genes[J]. 《Journal of Oral and Maxillofacial Surgery》, 2022, 32(6): 343-353.

https://journal06.magtech.org.cn/Jweb_joms/CN/Y2022/V32/I6/343

图/表 8

图1 OSCC和正常对照样本中ARGs的表达差异和基因功能富集分析 A. 火山图显示 ARGs,其中红色、蓝色和灰色分别代表上调、下调和无显著差异的基因;B. 热图显示 37 个具有差异表达的 ARGs;C. 37 个具有差异表达的 ARGs的表达水平,其中红色和绿色分别代表肿瘤和正常对照样本;D. GO 分析;E. KEGG 分析。

Figure 1 The expression difference and gene functional enrichment analysis of ARGs in OSCC and normal samples

图2 差异表达的ARGs的单因素Cox回归分析及LASSO回归分析 A. 单因素Cox回归分析;B.选择LASSO模型中的最佳参数(λ);C. 6个预后相关ARGs的LASSO系数分布。

Figure 2 Univariate Cox regression and LASSO regression analysis of ARGs with differential expression

图3 基于ARGs的风险评分模型在TCGA数据集显示出良好的预测能力 A. 风险评分分布、生存状态分布和6个预后基因的表达谱;B. 时间依赖的ROC 曲线;C. 生存分析。

Figure 3 ARGs-based risk score model displays favorable prediction ability in the TCGA datasets

图4 基于ARGs的风险评分模型在GEO验证集显示出良好的预测能力 A. 风险评分分布、生存状态分布和6个预后基因的表达谱;B. 时间依赖的ROC 曲线;C. 生存分析。

Figure 4 ARGs-based risk score model displays favorable prediction ability in the GEO verification datasets

图5 独立预后因素评估 A. 单因素 Cox 回归分析;B. 多因素 Cox 回归分析;C. ROC 曲线。

Figure 5 Evaluation of independent prognostic factors

图6 OSCC患者组间风险评分分布 A. 年龄;B. 性别;C. 病理分级;D. 肿瘤分期;E. T 分期;F. N 分期。

Figure 6 Risk score distribution among OSCC patients

图7 构建列线图预测OSCC患者1、3、5 年生存率 A. 构建用于估计 OSCC 患者预后的列线图;B—D. 分别为1、3、5 年预测生存率的校正图。

Figure 7 Nomogram is constructed to predict 1-, 3-, and 5-year survival rate of OSCC patients

图8 免疫细胞浸润模式 A. 高、低风险组间免疫细胞浸润的差异;B. 高、低风险组CD8⁺T细胞浸润比例差异。

Figure 8 Immune cell infiltration pattern

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