基于生物信息学的肝内胆管癌差异表达基因谱中关键基因的筛选及分析
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湖南医药学院医学院,湖南 怀化 418000

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陈伟毅,湖南医药学院讲师,主要从事消化系统肿瘤防治方面的研究。

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湖南省自然科学基金资助项目(2022JJ50290);湖南省卫生健康委科研课题基金资助项目(202104081483);2021、2022年度湖南省大学生创新创业训练计划资助项目(4314、5021);湖南医药学院2020年科研孵化库建设项目第二批基金资助项目。


Screening and analysis of key genes in differential expression gene profile of intrahepatic cholangiocarcinoma based on bioinformatics
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Medical College, Hunan University of Medicine, Huaihua Hunan 418000, China

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    摘要:

    背景与目的 肝内胆管癌(ICC)是指来源于肝内胆管上皮的一种恶性肿瘤,其发病隐匿,恶性程度高。ICC早期无明显临床表现,大多数患者确诊时往往已失去手术机会,因此预后极差。寻找ICC的早期诊断和治疗靶标具有重要意义,因此,本研究通过生物信息学方法对影响ICC发生发展的关键基因进行筛选。方法 从GEO数据库下载2个ICC转录组数据集(GSE107943、GSE119336)。用R语言的edgeR包筛选出差异表达基因,然后对这些差异表达基因进行GO和KEGG通路富集分析。通过STRING数据库建立差异表达基因的蛋白质-蛋白质相互作用(PPI)网络,使用Cytoscape中的MCODE插件发掘出关键调控基因。分析关键调控基因在肿瘤组织中的表达,采用UALCAN、GEPIA数据库进行验证。通过UCSC XENA数据库分析关键调控基因在泛癌中的表达。利用TCGA数据库分析关键调控基因共表达基因。采用UALCAN、GEPIA数据库分析关键调控基因与患者预后、肿瘤分级、分期、淋巴转移的关系。使用R语言GSVA包计算关键调控基因表达与免疫浸润相关性。绘制ROC曲线评价关键调控基因对ICC的预测能力。采用细胞实验验证关键调控基因的表达。结果 共筛选出1 094个共同差异表达基因,其中共同上调的基因为567个,共同下调的基因为527个,主要参与小分子分解代谢、有机酸生物合成、碳代谢等过程。通过PPI网络挖掘出3个关键调控基因Polo样激酶1(PLK1)、羟基酸氧化酶2(HAO2)、纤维胶凝蛋白(FCN2),其中PLK1基因在肿瘤组织中明显上调,HAO2和FCN2基因在肿瘤组织明显下调,通过UALCAN、GEPIA数据库验证发现3个基因表达与分析结果一致。通过UCSC XENA数据库分析发现PLK1在28种肿瘤中表达显著增高,HAO2在24种肿瘤表达显著降低,FCN2在27种肿瘤表达显著降低。TCGA数据库分析发现PLK1与CCNA2、GTSE1等基因共表达,HAO2与MTTP、CPS1等基因共表达,FCN2与FAM99A、GDF2等基因共表达。UALCAN数据库分析发现3个基因表达与肿瘤的分期和分级、淋巴转移有关,其中PLK1高表达、HAO2、FCN2低表达提示肿瘤分期更高、分化更差、更易出现淋巴转移。相关性分析发现PLK1表达与Th2 cells浸润呈显著正相关,FCN2表达与aDC浸润呈显著负相关。绘制ROC曲线显示这3个基因都可以很好地诊断ICC,其中HAO2的诊断能力最好。细胞实验结果发现PLK1在RBE中表达明显升高,HAO2、FCN2在RBE中表达明显降低(均P<0.01)。结论 PLK1、HAO2、FCN2可能是影响ICC发生发展的关键调控基因,这3个基因可能成为ICC诊治的新靶点。

    Abstract:

    Background and Aims Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor arising from the intrahepatic bile duct epithelium, which has an insidious onset and high degree of malignancy. Because there are no obvious clinical symptoms in the early stage of ICC, and most patients have lost the opportunity for surgery at the time of diagnosis, so its prognosis is very poor. Exploration of targets for early diagnosis and treatment of ICC is of great significance. Therefore, this study was conducted to screen the key genes involved in the occurrence and development of ICC.Methods Two ICC transcriptome datasets (GSE107943, GSE119336) were downloaded from the GEO database. The differentially expressed genes were screened by edgeR package of R language, and then these genes underwent GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) networks of these genes was constructed by using STRING database, and the key protein regulatory genes were mined by using the MCODE plug-in of Cytoscape. The expressions of key protein regulatory genes in tumor tissues were analyzed and verified by UALCAN and GEPIA databases. UCSC XENA database was used to analyze the expressions of key regulatory genes in generalized carcinoma. TCGA database was used to analyze the co-expression genes of the key regulatory genes. UALCAN and GEPIA databases were used to analyze the relationship between key regulatory genes and patient prognosis, tumor grade, stage and lymphatic metastasis. The correlation between the expressions of the regulatory genes and immune infiltration were calculated using R language GSVA package. The predictive abilities of the key protein regulatory genes to ICC were evaluated by drawing ROC curve. Cell experiments were performed to verify the expressions of the key regulatory genes.Results A total of 1 094 common differentially expressed genes were screened, including 567 up-regulated genes and 527 down-regulated genes, which were mainly involved in the process of small molecule catabolism, organic acid biosynthesis, carbon metabolism and so on. Three key genes Polo-like kinase 1(PLK1), hydroxyacid oxidase 2(HAO2)and ficolin-2(FCN2) were mined through PPI networks. PLK1 gene was significantly up-regulated in tumor tissues, and HAO2 and FCN2 genes were significantly down-regulated in tumor tissues, which were verified by UALCAN and GEPIA databases. The analysis of UCSC XENA database showed that the expression of PLK1 was significantly increased in 28 types of tumors, the expression of HAO2 was significantly decreased in 24 types of tumors, and the expression of FCN2 was significantly decreased in 27 types of tumors. The analysis of TCGA database showed that PLK1 was co-expressed with CCNA2 and GTSE1, HAO2 was co-expressed with MTTP and CPS1, and FCN2 was co-expressed with FAM99A and GDF2. The analysis of the UALCAN database found that the expression of three genes was related to the stage and grade of the tumor and lymph node metastasis. Among them, high expression of PLK1, low expression of HAO2 and FCN2 were associated with higher tumor stage, worse differentiation and more prone to lymph node metastasis. The correlation analysis found that the expression of PLK1 was significantly positively correlated with the infiltration of Th2 cells, and the expression of FCN2 was significantly negatively correlated with the infiltration of aDC cells. The ROC curve showed that all the three genes could diagnose ICC well, among which HAO2 had the best diagnostic ability. The results of cell experiments showed that the expression of PLK1 was significantly increased, while the expression of HAO2 and FCN2 were significantly decreased in RBE (all P<0.01).Conclusion LK1, HAO2 and FCN2 may be the key protein regulatory genes involved in the occurrence and progression of ICC. These three genes may probably become new targets for the diagnosis and treatment of ICC.

    表 2 两个数据集中差异表达最大的5个共同上调和下调基因Table 2 The five co-up-regulated and co-down-regulated genes
    表 4 关键蛋白调控功能基因节点信息Table 4 Node information of the key genes
    表 1 ICC转录组数据信息和样本分类Table 1 ICC transcriptome data information and sample classification
    图1 ICC差异表达基因的分析结果 A:GSE107943差异表达基因火山图;B:GSE119336差异表达基因火山图;C:两个数据集共同差异表达基因 Venn图;D:两个数据集共同上调基因 Venn图;E:两个数据集共同下调基因 Venn图Fig.1 Results of analysis of differentially expressed genes in ICC A: Volcano map of GSE107943; B: Volcano map of GSE119336; C: Venn diagram of differentially expressed genes both in GSE107943 and GSE119336; D: Venn diagram of up-regulated differentially expressed genes; E: Venn diagram of down-regulated differentially expressed genes
    图2 ICC共同差异表达基因的功能和通路富集 A:共同差异表达基因的GO—BP富集;B:共同差异表达基因的GO—CC富集;C:共同差异表达基因的GO—MF富集;D:共同差异表达基因的KEGG通路富集Fig.2 GO and KEGG enrichment of common differentially expressed genes in ICC A: GO—BP enrichment; B: GO—CC enrichment; C: GO—MF enrichment; D: GO—BP enrichment
    图3 ICC差异表达基因的PPI网络Fig.3 PPI network of the differentially expressed genes in ICC
    图4 ICC差异表达基因PPI网络中3个关键子网络Fig.4 Three key subnetworks in ICC differential expression gene PPI network
    图5 关键调控基因的表达分析及验证 A:PLK1、HAO2、FCN2在ICC组织和正常组织中的表达差异;B:PLK1、HAO2、FCN2在UALCAN数据库中的表达差异;C:PLK1、HAO2、FCN2在GEPIA数据库中的表达差异Fig.5 Expression analysis and validation of the key regulatory genes A: The expression differences of PLK1, HAO2 and FCN2 between ICC and normal tissues; B: The expression differences of PLK1, HAO2 and FCN2 in UALCAN database; C: The expression differences of PLK1, HAO2 and FCN2 in GEPIA database
    图6 PLK1、HAO2、FCN2在肿瘤中的表达情况分析 A:PLK1在28/33种肿瘤中表达上调;B:HAO2在24/33种肿瘤中表达下调;C:FCN2在27/33种肿瘤中表达下调Fig.6 Analysis of the expressions of PLK1, HAO2 and FCN2 in tumors A: Up-regulation of PLK1 in 28/33 tumors; B: Down-regulation of HAO2 in 24/33 tumors; C: Down-regulation of FCN2 in 27/33 tumors
    图7 关键调控基因在ICC中的共表达基因分析 A:PLK1共表达基因热图;B:HAO2共表达基因热图;C:FCN2共表达基因热图;D:共表达基因和关键基因的PPI网络;E:共表达基因GO和KEGG富集分析Fig.7 Co-expression gene analysis of the key regulatory genes in ICC A: Heat map of PLK1 co-expressed genes; B: Heat map of HAO2 co-expressed genes; C: Heat map of FCN2 co-expressed genes; D: PPI network of co-expressed genes and key regulatory genes; E: GO and KEGG enrichment of co-expressed genes
    图8 关键调控基因对ICC发展、预后的影响 A:PLK1、HAO2、FCN2对肿瘤分级的影响;B:PLK1、HAO2、FCN2对肿瘤分期的影响;C:PLK1、HAO2、FCN2对肿瘤淋巴转移的影响;D:PLK1、HAO2、FCN2对患者预后的影响Fig.8 Effects of the key regulatory genes on the development and prognosis of ICC A: Effect of PLK1, HAO2, FCN2 on tumor grade; B: Effect of PLK1, HAO2, FCN2 on tumor stages; C: Effect of PLK1, HAO2, FCN2 on tumor nodal metastasis; E: Effect of PLK1, HAO2, FCN2 on prognosis of patients
    图9 关键调控基因对ICC免疫浸润水平的影响 A:PLK1、HAO2、FCN2表达与免疫细胞浸润水平的相关性;B:PLK1表达与Th2细胞、NK细胞浸润相关性;C:HAO2表达与中性粒细胞、CD8 T细胞浸润相关性;D:FCN2表达与Tcm、aDC浸润相关性Fig.9 Effect of key regulatory genes on ICC immune infiltration level A: Correlation between PLK1, HAO2, FCN2 expression and immune cell infiltration level; B: Correlation between PLK1 expression and Th2 cells as well as NK cell infiltration; C: Correlation between HAO2 expression and neutrophils as well as CD8 T cells infiltration; D: Correlation between FCN2 expression and Tcm as well as aDC
    图10 关键调控基因对ICC的诊断能力分析Fig.10 Diagnostic abilities of the key regulatory genes for ICC
    图11 关键调控基因在HIBEC和RBE表达差异 A:两种细胞株PLK1、HAO2、FCN2 mRNA表达差异;B:两种细胞株PLK1、HAO2、FCN2蛋白表达差异Fig.11 Differential expression of key regulatory genes in HIBEC and RBE A: Differential expression of PLK1, HAO2, FCN2 mRNA in two cell lines; B: Differential expression of PLK1, HAO2, FCN2 protein in two cell lines
    表 3 得分前三的子网络模块信息Table 3 Information of the top 3 sub-network modules
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陈伟毅,陈立军.基于生物信息学的肝内胆管癌差异表达基因谱中关键基因的筛选及分析[J].中国普通外科杂志,2022,31(8):1048-1063.
DOI:10.7659/j. issn.1005-6947.2022.08.008

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  • 收稿日期:2022-02-23
  • 最后修改日期:2022-07-22
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  • 在线发布日期: 2022-09-02