基于GEO数据库探究结直肠肿瘤相关基因及其功能

doi:10.6040/j.issn.1671-7554.0.2023.1090

摘要/Abstract

摘要： 目的利用生物信息学分析方法,结合GEO数据库,探讨获取结直肠肿瘤关键基因的差异表达情况。方法使用GEO数据库分析结直肠肿瘤患者的肿瘤组织和正常组织基因表达数据,使用GEO2R筛选差异表达基因(differential expression genes, DEGs),利用David数据库对DEG进行基因本体论(gene ontology, GO)富集分析,获得DEG的分子功能、细胞组分和生物过程结果,利用基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)获取差异基因在疾病状态下的信号通路信息,构建差异基因间蛋白质相互作用网络,并使用Cytoscape软件对网络中的关键节点进行拓扑分析,筛选出结直肠肿瘤患者发生过程中的关键基因。结果通过对GSE21510数据集的生物信息学分析,共鉴定到664个DEGs,其中结直肠肿瘤患者高表达基因234个,低表达基因430个。这些DEGs主要参与细胞分裂、RNA聚合酶II启动子转录正和负调控等生物过程,同时参与细胞核、胞质、细胞质、核浆、细胞膜等细胞成分组成,并参与蛋白质绑定、ATP绑定等分子功能的表达和实现等过程,通过蛋白质网络分析确定高表达基因CDK1、CCNB1、TOP2A、AURKA、UBE2C、BUB1、CHEK1、RRM2、MYC、TPX2和低表达基因SLC26A3、CLCA4、GUCA2A、MS4A12、ZG16、GUCA2B、CLCA1、AQP8、MT1E、MT1G为关键基因。生存分析结果显示,关键基因CCNB1低表达与CRC患者预后较差显著相关(logrank P<0.01);此外,CCNB1基因与肿瘤病理分期显著相关(P<0.01)。结论所筛选的关键基因可能成为诊断或治疗结直肠肿瘤的的标志物或潜在靶点,本研究结果为结直肠肿瘤的发病机制、治疗方法等领域的研究提供了理论参考。

关键词: 结直肠肿瘤, GEO数据库, 数据库芯片, 生物信息学分析, 差异表达基因

Abstract: Objective To explore the differential expression genes(DEGs)in colorectal cancer(CRC)using bioinformatic analysis in combination with the Gene Expression Omnibus(GEO)database. Methods The gene expression data of tumor tissue and normal tissue of CRC patients were obtained from the GEO database. The DEGs were screened using GEO2R, which then underwent GO enrichment analysis using the David database to identify the molecular functions, cellular components and biological processes. The signaling pathways of the DEGs were obtained from the KEGG to construct protein interaction network. Topological analysis of key nodes in the network was performed using Cytoscape software to screen out key genes in the pathogenesis of CRC. Results Through bioinformatic analysis of the GSE21510 dataset, a total of 664 DEGs were screened, including 234 upregulated and 430 downregulated genes, which were mainly involved in the biological processes, such as cell division, positive and negative regulation of RNA polymerase II promoter transcription, as well as in the composition of cellular components, such as the nucleus, cytoplasm, cytosol, plasma, and cell membrane, and in processes such as the expression and realization of molecular functions of protein-binding, ATP-binding, and so on. Protein network analysis identified the upregulated genes, including CDK1, CCNB1, TOP2A, AURKA, UBE2C, BUB1, CHEK1, RRM2, MYC, and TPX2, and the downregulated genes, including SLC26A3, CLCA4, GUCA2A, MS4A12, ZG16, GUCA2B, CLCA1, AQP8, MT1E and MT1G. Survival analysis showed that the low expression of the key gene CCNB1 was significantly correlated with poor prognosis of CRC patients(logrank P<0.01). Moreover, CCNB1 was significantly correlated with tumor pathological stage(P<0.01). Conclusion The screened key genes may become markers or potential targets for the diagnosis or treatment of CRC, which provide theoretical reference for the research on the pathogenesis and treatment of CRC.

Key words: Colorectal cancer, Gene Expression Omnibus, Database chip, Bioinformatic analysis, Differential expression genes

中图分类号:

R574

景凯,徐志伟,李乐平. 基于GEO数据库探究结直肠肿瘤相关基因及其功能[J]. 山东大学学报 (医学版), 2024, 62(1): 21-30.

JING Kai, XU Zhiwei, LI Leping. Exploring colorectal cancer related genes and their functions based on GEO[J]. Journal of Shandong University (Health Sciences), 2024, 62(1): 21-30.

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