| 结肠癌核心基因生物信息学分析及验证 |
| 投稿时间：2024-11-15 修订日期：2025-01-03 点此下载全文 |
| 引用本文：金庆,尚士宣,丁佑铭.结肠癌核心基因生物信息学分析及验证[J].医学研究杂志,2025,54(6):26-33 |
| DOI：
10.11969/j.issn.1673-548X.2025.06.006 |
| 摘要点击次数: 17 |
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| 基金项目:国家重点研发计划项目(2022YFC2407304) |
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| 中文摘要:目的 筛选结肠癌相关的核心基因,为结肠癌分子诊断、靶向治疗、预后评价等提供新的研究方向。方法 从基因表达数据库中下载与结肠癌相关的数据集(GSE62932、GSE110224)并筛选出结肠癌差异表达基因(differentially expressed genes,DEGs),利用Metascape数据库对DEGs进行基因本体论(gene ontology,GO)功能注释分析和京都基因与基因组百科全书(Kyoto encyclopediaof genes and genomes,KEGG)通路富集分析。使用 String 数据库和 Cytoscape 软件构建蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络图,筛选出核心基因,利用Kaplan-Meier plotter数据库对筛选出的核心基因进行生存分析,使用GEPIA2数据库对核心基因进行表达量验证。使用Metascape数据对最终筛选出的核心基因重新进行KEGG通路富集分析,明确核心基因富集的信号通路；选择核心基因中评分最高的COL1A1进行免疫组化验证。结果 共鉴定出468个DEGs,其中包括162个上调DEGs和306个下调DEGs。在这两个数据集中,共同表达的基因有194个,其中共同表达上调的基因有54个,共同表达下调的基因有140个。借助CytoHubba插件筛选出了在评分中位列前10的基因节点,将其确定为结肠癌的可能核心基因(COL1A1、MMP3、COL1A2、COL11A1、THBS2、BGN、MMP1、COL10A1、SPP1、INHBA)；对这10个基因进行预后及表达量分析,10个核心基因在结肠癌组织及健康结肠组织中的表达量差异均有统计学意义,10个核心基因除MMP1基因外均与结肠癌预后相关,其中MMP3为正相关关系,其余8个基因均呈负相关关系。KEGG通路富集分析结果表明,核心基因主要富集于肌肉细胞中的细胞骨架、细胞外基质受体相互作用以及蛋白质消化和吸收3个信号通路；免疫组化分析结果显示,COL1A1在结肠癌组织中的表达水平高于癌旁组织。结论 生物信息分析筛选的核心基因COL1A1、MMP3、COL1A2、COL11A1、THBS2、BGN、MMP1、COL10A1、SPP1、INHBA通过影响肿瘤微环境在结肠癌的发生、发展过程中发挥作用,有望成为具有临床价值的结肠癌生物学标志物和治疗靶标。 |
| 中文关键词:结肠癌 生物信息学分析 差异表达基因 核心基因 诊断 预后 |
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| Bioinformatics Analysis and Verification of Core Genes of Colon Cancer. |
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| Abstract:Objective To screen the core genes related to colon cancer in order to provide a new research direction for molecular diagnosis, targeted therapy and prognosis evaluation of colon cancer. Methods Colon cancer differentially expressed genes (DEGs) were screened out from the gene expression database (GEO), and functional annotation and pathway enrichment analysis of DEGs were performed by gene ontology (GO)and Kyoto encyclopedia of genes and genomes(KEGG) analysis in Metascape database. The network of protein interactions (PPI) was built utilizing the String database and Cytoscape software to identify the core genes. Survival analysis of selected core genes was performed using Kaplan-Meier plotter database, and expression level of core genes was verified using GEPIA2database. Metascape data was used to conduct KEGG pathway enrichment analysis on the selected core genes, and the signal pathway of core gene enrichment was identified. COL1A1 in the core gene was selected for immunohistochemical verification. Results A total of 468 DEGs were identified, including 162 UDEGs and 306 DDEGs. In the two datasets, there were 194 overlapping genes, of which 54 were up-regulated and 140 were down-regulated. With the help of CytoHubba plugin, the top 10gene nodes in the score were selected and identified as the possible core genes of colon cancer (COL1A1, MMP3, COL1A2, COL11A1, THBS2, BGN, MMP1, COL10A1, SPP1, INHBA). The prognosis and expression levels of these 10genes were analyzed, and the expression levels of the 10 core genes were different in colon cancer tissues and normal colon tissues. All of the 10 core genes were correlated with colon cancer prognosis except MMP1, among which MMP3 was positively correlated and the other 8genes were negatively correlated. KEGG analysis showed that core genes were mainly concentrated in three signaling pathways:cytoskeleton, extracellular matrix (ECM) receptor interaction and protein digestion and absorption in muscle cells. Immunohistochemistry analysis revealed a marked increase in the expression level of COL1A1 within the tumor tissue compared to that in paracancer tissue. Conclusion The core genes COL1A1, MMP3, COL1A2, COL11A1, THBS2, BGN, MMP1, COL10A1, SPP1 and INHBA selected by bioinformation analysis may be involved in the occurrence and development of colon cancer, and may be related to the prognosis of patients with colon cancer. It is anticipated that it will serve as a clinically significant biomarker and a therapeutic target for colon cancer. |
| keywords:Colon cancer Bioinformatics Differentially expressed gene Core genes Diagnosis Prognosis |
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