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中华腔镜泌尿外科杂志(电子版) ›› 2018, Vol. 12 ›› Issue (05) : 347 -351. doi: 10.3877/cma.j.issn.1674-3253.2018.05.014

所属专题： 文献；

实验研究

基于芯片微阵列数据库对无精子症相关基因挖掘及生物信息学分析
邹自灏1, 邓楠2, 潘兆君2, 代冉冉2, 郑文忠3, 刘平2, 毛向明1,†()   
  1. 1. 510280　广州，南方医科大学珠江医院泌尿外科
    2. 510150　广州，广州医科大学附属第三医院泌尿外科
    3. 510150　广州，广州医科大学附属第三医院麻醉科
  • 收稿日期:2017-07-31 出版日期:2018-10-01
  • 通信作者: 毛向明

Identification of key candidate genes and pathways in azoospermia by integrated GEO microarray database and bioinformatical analysis

Zihao Zou1, nan Deng2, Zhaojun Pan2, Ranran Dai2, Wenzhong Zheng3, Ping Liu2, Xiangming Mao1,†()   

  1. 1. Department of Urology, ZhuJiang Hospital of Southern Medical University, Guangdong 510280, China
    2. Department of Urology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
    3. Department of Anesthesiology, the Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
  • Received:2017-07-31 Published:2018-10-01
  • Corresponding author: Xiangming Mao
  • About author:
    Corresponding author: Mao Xiangming, Email:
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引用本文：

邹自灏, 邓楠, 潘兆君, 代冉冉, 郑文忠, 刘平, 毛向明. 基于芯片微阵列数据库对无精子症相关基因挖掘及生物信息学分析[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2018, 12(05): 347-351.

Zihao Zou, nan Deng, Zhaojun Pan, Ranran Dai, Wenzhong Zheng, Ping Liu, Xiangming Mao. Identification of key candidate genes and pathways in azoospermia by integrated GEO microarray database and bioinformatical analysis[J/OL]. Chinese Journal of Endourology(Electronic Edition), 2018, 12(05): 347-351.

目的

从分子水平探讨非梗阻性无精子症的发病机制，为临床诊疗提供新思路。

方法

从基因表达综合数据库（GEO）中下载人非梗阻性无精子症的相关基因芯片数据，使用R语言对其进行归一化处理并筛选差异表达基因；使用DAVID及KEGG数据库对其进行差异基因本体功能及信号通路富集分析；通过Cytoscape绘制差异基因共表达网络并使用CytoHubba插件计算筛选核心基因（hub基因）；最后使用ClueGo及Centiscape对核心基因进行富集分析。

结果

R语言筛选出518个差异表达基因，其中上调基因271个、下调基因247个；本体功能及信号通路富集分析结果提示这些差异基因在精子发生、精子染色质凝聚、精子顶体膜及囊泡形成、精卵细胞识别、细胞分化、ATP偶联及转录因子结合等生物学过程中发挥重要作用；差异基因共表达网络分析发现GAPDHS，PCSK4，TSSK1B，TSSK2等hub基因在精子发生及分化过程中发挥关键作用。

结论

通过多维度挖掘GEO基因芯片数据并系统分析其内在信息，对明确非梗阻性无精子症发病的分子机制具有重要意义。

关键词: 无精子症, 数据库, 生物信息学, 精子, 基因, 富集分析
Objective

To explore the pathogenesis of non-obstructive azoospermia at the molecular level, aiming to provide novel ideas for clinical diagnosis and treatment.

Methods

The R language was utilized to normalize the gene chip data of non-obstructive azoospermia downloaded from the Gene Expression Omnibus (GEO) and screen the differentially expressed genes. DAVID and KEGG databases were employed to carry out ontology function of the differentially expressed genes and enrichment analysis of the signaling pathways. A differentially-expressed gene co-expression network was delineated by using Cytoscape. The hub genes were calculated and identified by using CytoHubba. Enrichment analysis of the hub genes was performed by using ClueGo and Centiscape.

Results

A total of 518 differentially expressed genes were screened by R language, of which 271 genes were up-regulated and 247 were down-regulated. The ontology function analysis and enrichment analysis of signaling pathways prompted that these differentially expressed genes played a pivotal role in the biological processes of spermatogenesis, sperm chromatin condensation, formation of sperm acrosome membrane and vesicle, sperm-egg recognition, cell differentiation, ATP coupling and transcription factor binding, etc. Analysis of the differentially expressed gene co-expression network demonstrated that the hub genes including GAPDHS, PCSK4, TSSK1B and TSSK2 played a key role in the spermatogenesis and differentiation processes.

Conclusion

Exploration of the GEO gene chip data from multiple dimensions and systematical analysis of the internal information are of significance to identify the molecular mechanism underlying non-obstructive azoospermia.

Key words: Azoospermia, Database, Bioinformatics, Sperm, Gene, Enrichment analysis
图1 差异基因筛选聚类热点图（部分）
图2 无精子症差异基因功能及通路富集
图3 NOA相关差异基因共表达网络构建及hub基因筛选
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