Chinese General Practice ›› 2022, Vol. 25 ›› Issue (08): 937-944.DOI: 10.12114/j.issn.1007-9572.2022.02.010

Special Issue: 肿瘤最新文章合集

• Original Research • Previous Articles     Next Articles

Bioinformatic Analysis of Potential Key Genes in Castration-resistant Prostate Cancer Development

  

  1. 1.Department of UrologyNorth China University of Science and Technology Affiliated HospitalTangshan 063000China

    2.School of Public HealthNorth China University of Science and TechnologyTangshan 063210China

    3.Modern Education Technology CenterNorth China University of Science and TechnologyTangshan 063000China

    *Corresponding authorsSHEN HongEngineerE-mailshenhong@ncst.edu.cn

    CAO FenghongChief physicianMaster supervisorE-mailcaofenghong@163.com

  • Received:2021-10-11 Revised:2021-12-20 Published:2022-03-15 Online:2022-03-02

去势抵抗性前列腺癌潜在关键基因的生物信息学分析

  

  1. 1.063000 河北省唐山市,华北理工大学附属医院泌尿外科
    2.063210 河北省唐山市,华北理工大学公共卫生学院
    3.063000 河北省唐山市,华北理工大学现代教育中心
  • 通讯作者: 沈宏,曹凤宏
  • 基金资助:
    河北省自然科学基金资助项目(H2019209595);河北省医学科学研究课题计划资助项目(20210212)

Abstract: Background

Castration-resistant prostate cancer (CRPC) is one of the most prevalent cancers in males with a high fatality rate. Its molecular mechanism is still unclear, and there is no effective treatment.

Objective

To explore the key genes involved in CRPC development using bioinformatic analysis, offering new ideas for the diagnosis and treatment of CRPC.

Methods

The data set GSE32269 which contains human primary prostate cancer and CRPC was downloaded from the Gene Expression Omnibus database for further bioinformatic analysis. R language was used to identify differentially expressed genes (DEGs) in CRPC. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were further performed by using DAVID. A protein-protein interaction (PPI) network of DEGs was constructed by using STRING database for screening potential key genes. And the identified potential key genes were further analyzed by survival analysis and receiver operating characteristic (ROC) curve analysis.

Results

279 DEGs were identified in microarray dataset GSE32269. GO enrichment analysis and KEGG pathway analysis revealed that cell division, mitosis and cell cycle signaling pathways may play an important role in the development of CRPC. PPI network screening revealed that there were 15 potential key genes, among which CDC20, MAD2L1 and NUSAP1 expressed differentially in CRPC patients: those with highly expressed CDC20, MAD2L1 and NUSAP1 had statistically lower overall survival rate and disease-free survival rate than did those with low expressed CDC20, MAD2L1 and NUSAP1 (P<0.05) . The area under the ROC curve of CDC20, MAD2L1 and NUSAP1 to predict the occurrence of CRPC were 0.933, 0.762, and 0.950, respectively, indicating that each of them may have a high diagnostic value for CRPC.

Conclusion

CDC20, MAD2L1 and NUSAP1 may be key candidate genes associated with the development of CRPC.

Key words: Prostatic neoplasms, Castration-resistant prostate cancer, Key genes, Bioinformatics

摘要: 背景

去势抵抗性前列腺癌(CRPC)是男性常见恶性肿瘤疾病之一,病死率高,分子机制仍不十分清楚,且无有效治疗药物。

目的

应用生物信息学方法挖掘CRPC发生、发展的关键基因,为其诊治提供新思路。

方法

从基因表达综合数据库(GEO)中下载关于人类原发性前列腺癌(PCa)和CRPC的数据集GSE32269并进行生物信息学分析。使用R语言鉴定CRPC的差异表达基因(DEGs)。通过DAVID软件对DEGs进行基因本体论(GO)富集分析及京都基因和基因组百科全书(KEGG)通路分析。利用STRING在线数据库构建蛋白质-蛋白质相互作用(PPI)网络进一步筛选关键基因,并对关键基因进行生存分析和受试者工作特征(ROC)曲线分析。

结果

通过对微阵列数据集GSE32269分析共筛选出279个DEGs,进一步通过GO富集分析和KEGG通路分析发现在CRPC发展中,细胞分裂、有丝分裂和细胞周期等信号通路发挥重要作用。PPI网络分析筛选出15个关键基因,对关键基因进行生存分析发现:CDC20、MAD2L1和NUSAP1高表达组CRPC患者总生存率和无病生存率均分别低于CDC20、MAD2L1和NUSAP1低表达组(P<0.05);且CDC20、MAD2L1和NUSAP1预测CPRC发生的ROC曲线下面积分别为0.933、0.762、0.950,提示其对CRPC具有较高的诊断价值。

结论

CDC20、MAD2L1和NUSAP1可能是参与CRPC发展的关键候选基因。

关键词: 前列腺癌, 去势抵抗性前列腺癌, 关键基因, 生物信息学

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