基础医学与临床 ›› 2019, Vol. 39 ›› Issue (10): 1397-1403.

• 研究论文 • 上一篇    下一篇

肾透明细胞癌差异基因的生物信息学分析与验证

周烨1,施倩倩2,赵友3,崔笠1,徐仁芳1   

  1. 1. 苏州大学附属第三医院
    2. 常州市第一人民医院
    3. 苏州大学附属第三医院泌尿外科
  • 收稿日期:2018-10-10 修回日期:2018-12-30 出版日期:2019-10-05 发布日期:2019-09-25
  • 通讯作者: 徐仁芳 E-mail:czyyxrf@163.com
  • 基金资助:
    国家自然科学基金青年科学基金项目

Identification of differentially expressed genes in renal clear cell carcinoma by integrated bioinformatical analysis

  • Received:2018-10-10 Revised:2018-12-30 Online:2019-10-05 Published:2019-09-25

摘要: 目的 通过生物信息学分析筛选肾透明细胞癌的潜在靶基因,验证并探讨其意义。 方法 运用生信学分析筛选基因芯片中肾透明细胞癌组织与正常肾组织间的差异基因,通过功能和通路富集分析和构建蛋白相互作用网络筛选核心基因;运用实时定量PCR、蛋白免疫印迹和免疫组织化学检测该基因在肾透明细胞癌组织和对应癌周正常肾组织中的表达水平;通过TCGA数据库分析该基因在肾透明细胞癌中的表达水平及其对生存预后的影响。 结果 生信学分析筛选出差异表达的核心基因GATA3,其在肾透明细胞癌组织中表达水平显著低于相对应的癌周正常肾组织(P<0.01)。TCGA数据显示GATA3在肾透明细胞癌中的表达水平显著降低(P<0.01),GATA3低表达的肾透明细胞癌患者的生存期显著降低(P<0.05)。 结论 生信学分析的使用能有助于筛选靶基因并分析其功能,GATA3的差异性表达可能对解释肾透明细胞癌发生、发展机制有一定帮助,并且可能被用作肾透明细胞癌的新型诊断标志物或治疗靶点。

关键词: 肾透明细胞癌, 生物信息学, 差异基因, GATA3, 基因表达

Abstract: Objective To screen the potential key candidate genes in clear cell renal cell carcinoma (ccRCC) by bioinformatical analysis and to confirm its expression level between ccRCC tissue and normal renal tissue. Methods Differentially expressed genes (DEGs) were identified, functional and pathway enrichment analysis were performed and DEGs-associated protein–protein interaction network (PPI) was constructed. Real-Time PCR, Western blot and Immunohistochemistry analysis were used to detect the expression level of one of the DEGs in ccRCC tissue and normal renal tissue. The results were validated from transcription level and survival analysis by TCGA database. Results GATA3 was screened by bioinformatical analysis. The results showed that GATA3 was downregulated in ccRCC tissue samples(P<0.01). The TCGA database data showed that the expression level of GATA3 in ccRCC samples was significantly lower than the normal samples(P<0.01) and the survival outcome of low expression GATA3 patients is significantly lower than the high expression patients(P<0.05). Conclusion Bioinformatical analysis could improve our understanding of the cause and underlying molecular events, the candidate gene GATA3 could be used for new therapeutic targets and diagnostic targets of ccRCC.

Key words: cell renal cell carcinoma, bioinformatical analysis, differentially expressed genes, GATA3, gene expression

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