基于铜死亡相关基因的口腔鳞状细胞癌生物信息学分析及预后模型构建

doi:10.12439/kqhm.1005-4979.2026.05.006

摘要/Abstract

摘要：

目的：

探讨铜死亡相关基因（cuproptosis-related genes，CRGs）在口腔鳞状细胞癌（oral squamous cell carcinoma，OSCC）中的预后价值及其与肿瘤微环境（tumor microenvironment，TME）的关系，并构建预后预测模型。

方法：

从癌症基因组图谱（the Cancer Genome Atlas，TCGA）数据库和基因表达综合数据库（Gene Expression Omnibus，GEO）获取OSCC转录组及临床数据。采用单因素Cox回归分析和Kaplan-Meier生存分析筛选预后相关CRGs；基于预后相关CRGs及其差异表达基因（differentially expressed genes，DEGs）分别进行无监督共识聚类分析；采用基因集变异分析（gene set variation analysis，GSVA）、单样本基因集富集分析（single-sample gene set enrichment analysis，ssGSEA）、基因本体（Gene Ontology，GO）富集分析及京都基因和基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）通路富集分析探讨其生物学功能；采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）-Cox回归分析构建预后风险模型，并利用受试者工作特征（receiver operating characteristic，ROC）曲线验证模型预测效能；采用CIBERSORT算法分析风险评分与免疫细胞浸润的关系。

结果：

共筛选出15个与OSCC预后相关的CRGs。基于预后相关CRGs可将OSCC患者分为3种分子亚型，不同亚型患者总生存期差异具有统计学意义（P=0.002）。进一步筛选获得24个DEGs，并据此将患者划分为2种基因亚型，2种基因亚型患者总生存期差异具有统计学意义（P<0.001）。功能富集分析显示，DEGs主要富集于细胞周期、DNA复制、氨基酸生物合成及铂类药物耐药等相关通路。最终构建由THBS1和DEFB1组成的预后风险模型，该模型在训练集、验证集及总体样本中均表现出一定的预测能力。风险评分与多种免疫细胞浸润水平及TME特征显著相关。

结论：

CRGs与OSCC患者预后及TME密切相关。基于CRGs构建的预后模型能够在一定程度上预测患者生存结局，可为OSCC预后评估及相关机制研究提供参考。

关键词: 口腔鳞状细胞癌, 铜死亡, 肿瘤微环境

Abstract:

Objective:

To investigate the prognostic value of cuproptosis-related genes (CRGs) in oral squamous cell carcinoma (OSCC), explore their associations with the tumor microenvironment (TME), and construct a prognostic prediction model.

Methods:

Transcriptomic and clinical data of OSCC were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognosis-related CRGs were identified by univariate Cox regression and Kaplan-Meier survival analyses. Unsupervised consensus clustering was performed based on prognosis-related CRGs and their differentially expressed genes (DEGs), respectively. Gene set variation analysis (GSVA), single-sample gene set enrichment analysis (ssGSEA), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to investigate the biological functions of the identified genes. A prognostic risk model was established using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis and evaluated by receiver operating characteristic (ROC) curve analysis. The relationship between risk scores and immune cell infiltration was assessed using the CIBERSORT algorithm.

Results:

A total of 15 prognosis-related CRGs were identified in OSCC. Based on the expression profiles of prognosis-related CRGs, patients were classified into three molecular subtypes with significantly different overall survival outcomes (P=0.002). Subsequently, 24 DEGs were identified, and patients were further categorized into two gene subtypes with significantly different overall survival (P<0.001). Functional enrichment analyses revealed that these DEGs were mainly involved in pathways related to cell cycle, DNA replication, amino acid biosynthesis, and platinum drug resistance. A prognostic risk model consisting of THBS1 and DEFB1 was established and demonstrated certain predictive performance in the training cohort, validation cohort, and entire cohort. In addition, risk scores were significantly associated with the infiltration levels of multiple immune cells and TME characteristics.

Conclusion:

CRGs are closely associated with the prognosis and TME of patients with OSCC. The prognostic model based on CRGs exhibits certain predictive performance and may provide a reference for prognostic evaluation and mechanistic studies in OSCC.

Key words: oral squamous cell carcinoma, cuproptosis, tumor microenvironment

中图分类号:

R782

袁超, 张竞飞, 李玲, 高百鑫, 刘代利, 张萌, 蔡圳. 基于铜死亡相关基因的口腔鳞状细胞癌生物信息学分析及预后模型构建[J]. 《口腔颌面外科杂志》, 2026, 36(5): 399-409.

YUAN Chao, ZHANG Jingfei, LI Ling, GAO Baixin, LIU Daili, ZHANG Meng, CAI Zhen. Bioinformatics analysis of oral squamous cell carcinoma based on cuproptosis-related genes and the construction of a prognostic model[J]. Journal of Oral and Maxillofacial Surgery(CN), 2026, 36(5): 399-409.

https://journal06.magtech.org.cn/Jweb_joms/CN/Y2026/V36/I5/399

图/表 7

图1 CRGs对OSCC患者的预后影响 A—O.依次为ATP7A、CDKN2A、DLD、DBT、DLST、FDX1、GLS、LIAS、LIPT1、MTF1、NFE2L2、NLRP3、PDHA1、PDHB、SLC31A1的高、低表达组患者的Kaplan-Meier生存曲线。

Figure 1 Prognostic impact of CRGs on patients with OSCC

图2 OSCC中CRGs亚型的鉴定 A. k=3时的无监督共识聚类分析矩阵热图；B. CDF曲线图；C.差值面积图；D.分布跟踪图；E. Kaplan-Meier生存分析图。

Figure 2 Identification of CRGs subtypes in OSCC

图3 CRGs亚型的免疫浸润及功能富集分析 A—C. A与B、A与C、B与C亚型的GSVA富集热图；D.不同CRGs亚型间免疫细胞浸润差异分析，*表示P<0.05，**表示P<0.01，***表示P<0.001；E. PCA结果图；F.维恩图；G、H. DEGs的GO富集分析条形图和气泡图；I、J. DEGs的KEGG富集分析条形图和气泡图。

Figure 3 Immune infiltration and functional enrichment analyses of CRGs subtypes

图4 DEGs共识聚类分析图 A. k=2时的无监督共识聚类矩阵热图；B.共识聚类分析CDF曲线图；C.差值面积图；D.分布跟踪图；E. Kaplan-Meier生存分析图。

Figure 4 Consensus clustering analysis diagram of DEGs

图5 风险评分模型的构建与验证 A. LASSO系数路径图；B. LASSO回归分析交叉验证曲线；C—E.依次为总样本、验证集和训练集的ROC曲线图；F—H.依次为总样本、验证集和训练集高、低风险组的生存曲线。AUC：曲线下面积（area under the curve）。

Figure 5 Construction and validation of the prognostic risk model

图6 OSCC患者预后列线图的构建与验证 A、B.依次为列线图模型和验证曲线。

Figure 6 Construction and validation of the prognostic nomogram for patients with OSCC

图7 风险评分与免疫细胞浸润及TME特征的相关性分析 A—L.依次为样本中调节性T细胞、γδT细胞、滤泡辅助性T细胞、CD8⁺ T细胞、浆细胞、静息NK细胞、活化NK细胞、静息肥大细胞、静息记忆CD4⁺ T细胞、M0型巨噬细胞、活化肥大细胞、静息树突状细胞的风险评分相关性分析；M. TME小提琴图，***表示P<0.001，与低风险组比较；N.免疫细胞浸润丰度与建模基因、风险评分相关性热图，*表示P<0.05，**表示P<0.01，***表示P<0.001。

Figure 7 Correlation of risk scores with immune cell infiltration and TME characteristics

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