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硫利达嗪抗宫颈癌的潜在作用机制

谢芹,  赵春阳,  张葆溯,  赵洪波

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文章导航 > 昆明医科大学学报  > 2022 >  43(3): 13-20
谢芹, 赵春阳, 张葆溯, 赵洪波. 硫利达嗪抗宫颈癌的潜在作用机制[J]. 昆明医科大学学报, 2022, 43(3): 13-20. doi: 10.12259/j.issn.2095-610X.S20220317
引用本文: 谢芹, 赵春阳, 张葆溯, 赵洪波. 硫利达嗪抗宫颈癌的潜在作用机制[J]. 昆明医科大学学报, 2022, 43(3): 13-20. doi: 10.12259/j.issn.2095-610X.S20220317
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Qin XIE, Chunyang ZHAO, Baosu ZHANG, Hongbo ZHAO. Potential Mechanism of Thioridazine in Anti-cervical Cancer[J]. Journal of Kunming Medical University, 2022, 43(3): 13-20. doi: 10.12259/j.issn.2095-610X.S20220317
Citation: Qin XIE, Chunyang ZHAO, Baosu ZHANG, Hongbo ZHAO. Potential Mechanism of Thioridazine in Anti-cervical Cancer[J]. Journal of Kunming Medical University, 2022, 43(3): 13-20. doi: 10.12259/j.issn.2095-610X.S20220317
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硫利达嗪抗宫颈癌的潜在作用机制

doi: 10.12259/j.issn.2095-610X.S20220317

  • 昆明医科大学生物医学工程研究院暨云南省干细胞和再生医学重点实验室，云南 昆明　650500

基金项目: 云南省科技厅-昆明医科大学应用基础研究联合专项基金资助项目（202101AY070001-075）；云南省教育厅科学研究基金资助项目（2021J0226）
详细信息
    作者简介:

    谢芹（1997～），女，四川安岳人，在读硕士研究生，主要从事生物信息，网络药理学研究工作

    通讯作者:

    赵洪波，E-mail：zhaohongbo@kmmu.edu.cn

  • 中图分类号: R737.3

Potential Mechanism of Thioridazine in Anti-cervical Cancer

  • Institute of Biomedical Engineering & Yunnan Key Laboratory of Stem Cell and Regenerative Medicine，Kunming Medical University，Kunming Yunnan 650500，China

    摘要
  • 摘要:   目的  运用生物信息学分析， 探讨硫利达嗪抗宫颈癌的潜在作用机制。  方法  通过PharmMapper工具预测能够与硫利达嗪相互作用的靶基因，然后使用STRING在线工具对靶基因进行通路和组织表达富集分析。使用GeneCards和DisGeNET数据库筛选宫颈癌相关基因，与硫利达嗪的靶基因取交集，得到硫利达嗪可能作用于宫颈癌的交互基因。通过STRING构建蛋白互作（PPI）网络，推测核心靶点，评估其重要性。使用clusterProfiler软件进行GO和KEGG通路分析。  结果  通过PharmMapper预测得到47个靶基因，富集到肿瘤相关通路和宫颈癌细胞。与669个宫颈癌基因取交集，获得硫利达嗪和宫颈癌交互基因21个，其中10个关键节点基因为EGFR、PPARG、AR、NOS3、ALB、ESR1、MAPK1、MAPK14、ANXA5和MAPK8，且在宫颈癌PPI网络中处于重要位置。交互基因涉及到的生物进程主要有负离子转运的正调控、丝氨酸肽基磷酸化、类固醇代谢过程、血液凝固、细胞对化学应激的反应等。富集的KEGG通路包括调控癌症通路、松弛素信号通路、内分泌耐药、蛋白聚糖与肿瘤、细胞衰老、GnRH信号通路和VEGF信号通路等。  结论  硫利达嗪具有潜在的抗肿瘤作用，可能通过多靶点和多信号通路的方式在宫颈癌的治疗上发挥作用。
    Abstract:   Objective  To explore the potential mechanism of thioridazine in the treatment of cervical cancer by bioinformatics analysis.   Methods  The target genes that can interact with thioridazine was predicted by PharmMapper, and then conducted pathway and tissue expression enrichment analysis by STRING online tool. The cervical cancer related genes were screened through GeneCards and DisGeNET databases, and cross the target genes of thioridazine to obtain the interaction genes that thioridazine may act on cervical cancer. The protein-protein interaction（PPI） network was constructed using STRING, and the core targets were speculated and evaluated their importance．The GO and KEGG enrichment analysis were conducted using clusterProfiler package.   Results  A total of 47 target genes were predicted by PharmMapper, which were enriched in tumor-related pathways and cervical cancer cells. Intersection with 669 cervical cancer genes, 21 common genes were obtained, of which 10 key genes were EGFR, PPARG, AR, NOS3, ALB, ESR1, MAPK1, MAPK14, ANXA5 and MAPK8. These key genes were important in the cervical cancer PPI network. The biological processes involved in interactive genes mainly include positive regulation of anion transport, peptidyl-serine phosphorylation, steroid metabolic process, blood coagulation, and cellular response to chemical stress. Enriched KEGG pathways include pathways in cancer, relaxin signaling pathway, endocrine resistance, proteoglycans in cancer, cellular senescence, GnRH signaling pathway, and VEGF signaling pathway.   Conclusion  Thioridazine has potential anti-tumor effects and may play a role in the treatment of cervical cancer through multiple targets and multiple signaling pathways.
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  • 图  1  宫颈癌基因和硫利达嗪靶基因的Upset图

    Figure  1.  Upset map of cervical cancer genes and thioridazine target genes

    下载: 全尺寸图片 幻灯片

    图  2  交互基因的蛋白互作网络图

    Figure  2.  PPI network of interacting genes

    下载: 全尺寸图片 幻灯片

    图  3  关键靶基因的蛋白互作网络图

    Figure  3.  PPI network of key target genes

    下载: 全尺寸图片 幻灯片

    图  4  GO富集分析结果

    Figure  4.  Results of GO enrichment analysis

    下载: 全尺寸图片 幻灯片

    图  5  KEGG通路富集分析结果

    Figure  5.  Results of KEGG pathway enrichment analysis

    下载: 全尺寸图片 幻灯片

    表  1  排名前10的靶基因通路分析结果

    Table  1.   Top10 pathways results of target genes

    ID号名称基因数FDR
    hsa04926松弛素信号通路91.50E-08
    hsa05200肿瘤通路134.88E-08
    hsa01522内分泌耐药76.70E-07
    hsa05205蛋白聚糖与肿瘤83.01E-06
    hsa05166人类T细胞白血病
    病毒1感染    		84.18E-06
    hsa04218细胞衰老76.70E-06
    hsa04912促性腺激素释放
    激素信号通路    		66.70E-06
    hsa04914孕酮介导的卵
    母细胞成熟    		67.36E-06
    hsa05161乙型肝炎77.36E-06
    hsa04933糖尿病并发症中
    的AGE-RAGE信号通路    		67.85E-06
    下载: 导出CSV

    表  2  排名前10的靶基因疾病富集分析结果

    Table  2.   Top10 disease enrichment results of target genes

    ID号名称基因数FDR
    DOID：162肿瘤120.0022
    DOID：14566细胞增殖疾病130.0022
    DOID：4疾病310.0022
    DOID：65结缔组织疾病100.0064
    DOID：7解剖实体疾病250.0064
    DOID：0050636家族性内脏
    淀粉样变性    		30.0183
    DOID：28内分泌系统疾病70.0183
    DOID：0080001骨疾病80.0184
    DOID：11801蛋白质-能量营
    养不良    		20.0184
    DOID：0060075雌激素受体
    阳性乳腺癌    		20.0244
    下载: 导出CSV

    表  3  排名前10的靶基因组织表达富集分析

    Table  3.   Top10 tissue expression enrichment results of target genes

    ID号名称基因数FDR
    BTO：0000180宫颈癌细胞102.03E-06
    BTO：0000174胚胎结构222.03E-06
    BTO：0000759肝202.71E-06
    BTO：0000132血小板104.54E-06
    BTO：0001078胎盘164.54E-06
    BTO：0001546慢性淋巴细胞
    白血病细胞    		88.77E-06
    BTO：0001489全身469.93E-06
    BTO：0000345消化腺221.02E-05
    BTO：0001491脏器301.07E-05
    BTO：0000522腺体341.77E-05
    下载: 导出CSV

    表  4  关键靶基因在宫颈癌PPI网络中的排名

    Table  4.   Ranking of key target genes in the cervical cancer PPI network

    靶基因Degree值排名
    EGFR3296
    ALB27113
    ESR125520
    ANXA520032
    PPARG18237
    AR15953
    MAPK114372
    MAPK1414177
    MAPK813879
    NOS3105127
    合计525
    下载: 导出CSV
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