基于SSP1和TGFB1与食管腺癌发生、预后和免疫浸润关系的生物信息学分析

doi:10.13481/j.1671-587X.20240422

摘要/Abstract

摘要：

目的分析基因表达综合（GEO）数据库和癌症基因组图谱（TCGA）数据库中食管腺癌（EAC）基因表达数据，阐明EAC发病的潜在核心基因与肿瘤淋巴细胞浸润的关系，为EAC的诊断和治疗提供分子靶标。方法在GEO数据库检索“esophageal adenocarcinoma”，下载包括EAC和食管正常组织的高通量芯片数据集GSE13898、GSE26886、GSE74553和GSE92396。采用R软件的limma包筛选EAC组织和食管正常组织的差异表达基因（DEGs），并通过韦恩图获取共同DEGs，采用STRING数据库分析后导入Cytoscape软件筛选核心基因并构建蛋白-蛋白互作（PPI）网络，采用基因表达谱交互分析（GEPIA）数据库验证核心基因表达水平，采用阿拉巴马大学伯明翰分校癌症数据分析门户（UALCAN）和Kaplan-Meier Plotter数据库分析核心基因与EAC患者预后和临床资料的关联性，采用肿瘤免疫评价资源（TIMER）数据库分析核心基因与肿瘤免疫浸润的关系，采用基因本体论（GO）和京都基因与基因组百科全书（KEGG）对LinkedOmics数据库获得的核心基因中正相关表达基因进行功能和信号通路富集分析。结果对GEO获得的4个数据集的DEGs取交集，共获得340个DEGs，其中上调基因127个，下调基因213个。经STRING数据库和 Cytoscape 软件筛选后，最终获得评分最高的关键核心基因分泌型磷蛋白1（SPP1）和转化生长因子β1（TGFB1）。GEPIA数据库分析，与食管正常组织比较，癌组织中SPP1和TGFB1 mRNA表达水平明显升高（P<0.01）；SPP1低表达组EAC患者1、3和5年总体生存期均高于SPP1高表达组（HR=10.1，P<0.05；HR=3.09，P<0.05；HR=2.32，P<0.05），TGFB1低表达组EAC患者5年总体生存期高于TGFB1高表达组（HR=2.36，P<0.05）。UALCAN数据库分析，与食管正常组织比较，Ⅱ-Ⅲ期及N1-N2期淋巴结转移的EAC患者癌组织中SPP1和TGFB1 mRNA表达水平明显升高（P<0.01）。TIMER分析，SPP1和TGFB1 mRNA表达水平与EAC患者癌组织中巨噬细胞（r=0.353，P<0.01；r=0.187，P<0.05）和树突状细胞（r=0.236，P<0.01；r=0.221，P<0.01）浸润呈正相关关系。GO功能和KEGG信号通路富集分析，SPP1和TGFB1及其排名前50位正相关基因主要参与细胞迁移、细胞活性和血管发育等生物学过程及肿瘤蛋白多糖、细胞外基质（ECM）-受体互作和磷脂酰肌醇3-激酶（PI3K）/蛋白激酶B（AKT）等信号通路。结论 SPP1和TGFB1与EAC患者临床分期、淋巴结转移和总体生存期有密切关联。SPP1和TGFB1高表达可能导致巨噬细胞和树突状细胞浸润，从而改变肿瘤微环境。SPP1和TGFB1可能成为EAC诊断和治疗的新靶点。

关键词: 食管腺癌, 生物信息学, 生存分析, 预后, 肿瘤免疫浸润

Abstract:

Objective To analyze gene expression data of esophageal adenocarcinoma （EAC） in the Gene Expression Omnibus （GEO） and The Cancer Genome Atlas （TCGA） databases and clarify the relationship between the potential core genes and tumor lymphocyte infiltration in the EAC， and to provide the molecular targets for the diagnosis and treatment of EAC. Methods The high-throughput chip datasets GSE13898， GSE26886， GSE74553， and GSE92396， including EAC and normal esophageal tissues， were downloaded from the GEO database by searching for “esophageal adenocarcinoma”. The limma package of R software was used to screen the differentially expressed genes （DEGs） in EAC tissue and esophageal normal tissue， and the common DEGs were obtained through Venn diagram. After the DEGs were analyzed by STRING database， the results were imported into Cytoscape software to screen the core genes and construct the protein-protein interaction （PPI） network. The Gene Expression Profiling Interactive Analysis （GEPIA） database was used to verify the expression levels of core genes. The UALCAN and Kaplan-Meier Plotter databases were used to analyze the correlations between the core genes and prognosis and clinical data of the EAC patients. The Tumor Immune Estimation Resource （TIMER） database was used to analyze the relationship between core genes and tumor immune infiltration. Gene Ontology （GO） functional and Kyoto Encyclopedia of Genes and Genomes （KEGG） signaling pathway enrichment analysis were performed to analyze the positively correlated genes of core genes obtained from the LinkedOmics database. Results A total of 340 DEGs were obtained from the intersection of DEGs from the four GEO datasets， including 127 upregulated genes and 213 downregulated genes. After screening with the STRING database and Cytoscape software， the key core genes with the highest scores were secreted phosphoprotein 1 （SPP1） and transforming growth factor beta 1 （TGFB1）. The GEPIA database analysis results showed that compared with esophageal normal tissue， the expression levels of SPP1 and TGFB1 mRNA in cancer tissue were significantly increased （P<0.01）. The 1-year， 3-year， and 5-year overall survival of the EAC patients in SPP1 low expression group was higher than those in SPP1 high expression group （HR=10.1， P<0.05； HR=3.09， P<0.05； HR=2.32， P<0.05）， and the 5-year overall survival of the EAC patients in TGFB1 low expression group was higher than that in TGFB1 high expression group （HR=2.36， P<0.05）. The UALCAN database analysis results showed that compared with esophageal normal tissue， the expression levels of SPP1 and TGFB1 mRNA in cancer tissue of the EAC patients with stage Ⅱ-Ⅲ and N1-N2 lymph node metastasis were significantly increased （P<0.01）.The TIMER analysis results showed that the expression levels of SPP1 and TGFB1 mRNA in cancer tissue of the EAC patients were positively correlated with the infiltration of macrophages （r=0.353，P<0.01； r=0.187，P<0.05） and dendritic cells （r=0.236，P<0.01； r=0.221，P<0.01）. The GO and KEGG pathway enrichment analysis results showed that SPP1， TGFB1， and their top 50 positively correlated genes mainly participated in the biological processes such as cell migration， cell activity， and angiogenesis， and signaling pathways such as tumor proteoglycans， extracellular matrix （ECM）-receptor interaction， and phosphatidylinositol 3-kinase （PI3K）/protein kinase B （AKT）. Conclusion SPP1 and TGFB1 are closely associated with clinical staging， lymph node metastasis， and overall survival of the EAC patients. High expressions of SPP1 and TGFB1 may lead to the infiltration of the macrophages and dendritic cells， and change the tumor microenvironment. SPP1 and TGFB1 may become new targets for the diagnosis and treatment of EAC.

Key words: Esophageal adenocarcinoma, Bioinformatics, Survival analysis, Prognosis, Tumor immune infiltration

中图分类号:

R735.1

王元国,张鹏. 基于SSP1和TGFB1与食管腺癌发生、预后和免疫浸润关系的生物信息学分析[J]. 吉林大学学报(医学版), 2024, 50(4): 1076-1086.

Yuanguo WANG,Peng ZHANG. Bioinformatics analysis based on relationship between SSP1 and TGFB1 and occurrence, prognosis, and immune invasion of esophageal adenocarcinoma[J]. Journal of Jilin University(Medicine Edition), 2024, 50(4): 1076-1086.

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KEGG pathway	Count	P	Gene
Protein digestion and absorption	8	<0.01	COL1A1, COL3A1, COL1A2, COL5A1, COL24A1, COL11A1, COL7A1, COL10A1
AGE-RAGE signaling pathway in diabetic complications	6	<0.01	COL1A1, COL3A1, COL1A2, FN1, NOX4, MAPK12
Osteoclast differentiation	6	<0.01	FCGR3A, FCGR2A, NCF2, TREM2, FCGR1A, MAPK12
Leishmaniasis	5	<0.01	FCGR3A, FCGR2A, NCF2, FCGR1A, MAPK12
Neutrophil extracellular trap formation	6	<0.01	FCGR3A, FCGR2A, SIGLEC9, NCF2, FCGR1A, MAPK12
Platelet activation	5	<0.01	COL1A1, COL3A1, FCGR2A, COL1A2, MAPK12
Phagosome	5	<0.01	MSR1, FCGR3A, FCGR2A, NCF2, FCGR1A
ECM-receptor interaction	4	<0.01	COL1A1, COL1A2, SPP1, FN1
Fc gamma R-mediated phagocytosis	4	<0.01	HCK, FCGR3A, FCGR2A, FCGR1A
Diabetic cardiomyopathy	5	<0.01	COL1A1, COL3A1, COL1A2, NCF2, MAPK12
Proteoglycans in cancer	5	<0.01	COL1A1, COL1A2, PLAU, FN1, MAPK12
Amoebiasis	4	<0.01	COL1A1, COL3A1, COL1A2, FN1
Relaxin signaling pathway	4	<0.01	COL1A1, COL3A1, COL1A2, MAPK12
Tuberculosis	4	<0.05	FCGR3A, FCGR2A, FCGR1A, MAPK12
Focal adhesion	4	<0.05	COL1A1, COL1A2, SPP1, FN1
Staphylococcus aureus infection	3	<0.05	FCGR3A, FCGR2A, FCGR1A

KEGG pathway	Count	P	Gene
Focal adhesion	8	<0.01	COL1A1, CAV2, ACTN1, CAV1, COL6A2, TNC, ITGA5, PGF
Proteoglycans in cancer	6	<0.01	COL1A1, TGFB1, PLAU, CAV2, CAV1, ITGA5
ECM-receptor interaction	4	<0.01	COL1A1, COL6A2, TNC, ITGA5
Protein digestion and absorption	4	<0.01	COL1A1, COL6A2, COL5A3, COL7A1
PI3K-AKT signaling pathway	5	<0.05	COL1A1, COL6A2, TNC, ITGA5, PGF
Bacterial invasion of epithelial cells	3	<0.05	CAV2, CAV1, ITGA5
AGE-RAGE signaling pathway in diabetic complications	3	<0.05	COL1A1, TGFB1, SERPINE1
Amoebiasis	3	<0.05	COL1A1, TGFB1, ACTN1

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