影响口腔鳞状细胞癌预后潜在靶点的生物信息学分析

doi:10.12439/kqhm.1005-4979.2024.03.005

摘要/Abstract

摘要：

目的: 应用生物信息学技术对口腔鳞状细胞癌（oral squamous cell carcinoma，OSCC）基因芯片数据进行差异表达基因（differentially expressed genes，DEGs）筛选，进一步预测潜在靶点及预后基因。方法: 利用基因表达综合数据库（gene expression omnibus，GEO）下载基因芯片数据集（GSE23558和GSE138206），联合GEO2R工具在线分析OSCC与正常口腔黏膜组织的DEGs；并对其进行相关通路、蛋白质相互作用（protein-protein interaction，PPI）网络及关键网络节点分析，筛选前25个枢纽基因，通过多个外部数据库对其进行验证。应用Timer网站进一步分析枢纽基因与免疫细胞浸润及免疫检查点的关系，基于Lasso-Cox算法，构建相关基因的预后风险模型。构建包含预后风险模型和多个临床病理因素的列线图。结果: 分泌磷酸蛋白1 （secreted phosphoprotein 1，SPP1）、整合素α3 (integrin α3，ITGA3)基因在头颈部鳞状细胞癌（head and neck squamous cell carcinoma，HNSCC）中的表达水平与患者的不良预后相关（P<0.05）。ITGA3被认为是具有未来临床意义的潜在免疫治疗靶点。基于SPP1和ITGA3基因构建的预后模型，可以有效预测OSCC患者的预后。结论: ITGA3、SPP1可能为OSCC患者的治疗靶点及预后的生物标志物，并为探索OSCC的分子机制提供了理论依据。

关键词: 口腔鳞状细胞癌, 差异表达基因, 靶基因, ITGA3, 预后风险模型

Abstract:

Objective: To apply bioinformatics technology to screen differentially expressed genes (DEGs) in oral squamous cell carcinoma (OSCC) microarray data to further predict potential targets and prognostic genes. Methods: GSE23558 and GSE138206 datasets were downloaded from gene expression omnibus (GEO) database to analyze DEGs of OSCC and normal oral mucosa tissues online with the help of GEO2R; the associated pathways, protein-protein interaction (PPI) network and key network nodes were analyzed, and the top 25 hub genes were screened and verified through multiple external databases. The Timer website was used to analyze the relationship between hub genes and immune cell infiltration and immune checkpoints. Based on Lasso-Cox algorithm, a prognostic risk model of related genes was constructed. A nomogram containing prognostic risk model and multiple clinicopathological factors was constructed. Results: Integrin α3 (ITGA3) and secreted phosphoprotein 1 (SPP1) genes were highly expressed in head and neck squamous cell carcinoma (HNSCC) tissues and were correlated with poor prognosis of patients (P<0.05). ITGA3 was considered to be a potential immunotherapy target with future clinical significance. The prognostic model based on SPP1 and ITGA3 genes can effectively predict the prognosis of OSCC patients. Conclusion: ITGA3 and SPP1 may be biomarkers for the prognosis of OSCC patients. These results provide new clues for exploring the molecular mechanism and targeted therapy of OSCC.

Key words: oral squamous cell carcinoma, differentially expressed genes, target gene, ITGA3, prognostic risk signature

中图分类号:

R782

邹宪, 宋涛. 影响口腔鳞状细胞癌预后潜在靶点的生物信息学分析[J]. 《口腔颌面外科杂志》, 2024, 34(3): 193-201.

ZOU Xian, SONG Tao. Bioinformatics analysis of potential targets influencing the prognosis of OSCC[J]. 《Journal of Oral and Maxillofacial Surgery》, 2024, 34(3): 193-201.

https://journal06.magtech.org.cn/Jweb_joms/CN/Y2024/V34/I3/193

图/表 8

图1 GSE23558和GSE138206数据集中DEGs的火山图和韦恩图 A. GSE23558；B. GSE138206； C. DEGs的交集。

Figure 1 Volcano and Wayne diagrams of DEGs in GSE23558 and GSE138206 datasets

图2 差异基因的GO和KEGG富集分析 A.细胞组分注释；B.分子功能注释；C.生物学过程注释；D. KEGG分析。

Figure 2 GO and KEGG enrichment analysis of differential genes

图3 PPI网络和枢纽基因 A. PPI网络图；B.前25个枢纽基因子模块。

Figure 3 PPI network and hub genes

表1 前25个枢纽基因

Table 1 The top 25 hub genes

排名	名称	得分
1	ANLN	36
1	AURKA	36
1	NCAPG	36
1	KIAA0101	36
1	HMMR	36
6	DLGAP5	34
6	CDCA5	34
6	BIRC5	34
6	NEK2	34
6	CDC20	34
6	CCNB2	34
6	MELK	34
6	SPP1	34
14	KIF4A	32
15	MMP3	30
15	SHCBP1	30
15	KIF14	30
15	CXCL10	30
19	WDHD1	28
19	UHRF1	28
21	HGF	26
21	E2F7	26
21	STAT1	26
24	MMP1	24
25	ITGA3	20

图4 枢纽基因的生存曲线 A. ITGA3表达与预后的相关性；B. SPP1表达与预后的相关性。

Figure 4 Survival curve of hub genes

图5 ITGA3、SPP1在HNSCC和正常组织中的表达水平 A. GEPIA数据库中ITGA3、SPP1基因在HNSCC组织和正常组织中的表达；B. TCGA数据库中ITGA3、SPP1基因在HNSCC组织和正常组织中的表达；C. CPTAC数据库中 ITGA3、SPP1蛋白在HNSCC组织和正常组织中的表达；D. ITGA3、SPP1基因在各种肿瘤和其相对应正常组织中的表达。*表示P<0.05，**表示P<0.01，***表示P<0.001，与正常组织比较。

Figure 5 ITGA3、SPP1 expression levels in HNSCC and normal tissues

图6 肿瘤免疫相关分析 A. ITGA3表达与免疫细胞的关系；B. ITGA3表达与免疫检查点的关系。

Figure 6 Tumor immune correlation analysis

图7 Lasso-Cox回归筛选预后风险模型 A.每条曲线对应一个变量，显示其系数coefficients，X轴显示自变量lambda的值，Y轴表示自变量的系数；B.使用LASSO-Cox回归模型绘制了部分似然偏差与log（λ）的关系；C. HNSCC患者预后模型中基于ITGA3和SPP1的高、低危险组风险评分的分布(上)，高、低危险组之间生存时间和生存状态的分布（中）及HNSCC患者中2个风险基因(ITGA3和SPP1)在高风险组和低风险组的热图（下）；D.高风险组和低风险组HNSCC患者的Kaplan-Meier曲线；E.ROC曲线评价预后模型的性能。

Figure 7 Lasso-Cox regression for screening prognostic risk models

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