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薛澄, 周晨辰, 许晶, 杨博, 徐成钢, 戴兵, 刘亚伟, 孙丽君, 高翔, 郁胜强, 梅长林. 基于常染色体显性多囊肾病基因芯片数据的生物信息学分析[J]. 协和医学杂志, 2017, 8(2-3): 171-177. DOI: 10.3969/j.issn.1674-9081.2017.03.016
引用本文: 薛澄, 周晨辰, 许晶, 杨博, 徐成钢, 戴兵, 刘亚伟, 孙丽君, 高翔, 郁胜强, 梅长林. 基于常染色体显性多囊肾病基因芯片数据的生物信息学分析[J]. 协和医学杂志, 2017, 8(2-3): 171-177. DOI: 10.3969/j.issn.1674-9081.2017.03.016
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Cheng XUE, Chen-chen ZHOU, Jing XU, Bo YANG, Cheng-gang XU, Bing DAI, Ya-wei LIU, Li-jun SUN, Xiang GAO, Sheng-qiang YU, Chang-lin MEI. Bioinformatic Analysis of Microarray Data of Autosomal Dominant Polycystic Kidney Disease[J]. Medical Journal of Peking Union Medical College Hospital, 2017, 8(2-3): 171-177. DOI: 10.3969/j.issn.1674-9081.2017.03.016
Citation: Cheng XUE, Chen-chen ZHOU, Jing XU, Bo YANG, Cheng-gang XU, Bing DAI, Ya-wei LIU, Li-jun SUN, Xiang GAO, Sheng-qiang YU, Chang-lin MEI. Bioinformatic Analysis of Microarray Data of Autosomal Dominant Polycystic Kidney Disease[J]. Medical Journal of Peking Union Medical College Hospital, 2017, 8(2-3): 171-177. DOI: 10.3969/j.issn.1674-9081.2017.03.016
shu

基于常染色体显性多囊肾病基因芯片数据的生物信息学分析

Bioinformatic Analysis of Microarray Data of Autosomal Dominant Polycystic Kidney Disease

    摘要
  • 摘要:
      目的      通过GEO数据库(Gene Expression Omnibus)下载常染色体显性多囊肾病(autosomal dominant polycystic kidney disease, ADPKD)患者基因芯片数据集进行分析, 得出共同差异表达基因(differentially expressed genes, DEGs)并进行生物信息学分析, 探索ADPKD发病机制中可能的信号通路和蛋白-蛋白相互作用机制。
      方法     通过GEO数据库下载两组关于ADPKD患者肾囊肿组织及对照组织的基因芯片数据集GSE7869和GSE35831, 对其进行DEGs筛选, 使用DAVID数据库和Funrich软件分析生物学信息及信号通路, 使用STRING数据库分析蛋白-蛋白相互作用机制。
      结果     GSE7869共有3970个DEGs, GSE35831共有147个DEGs。两组DEGs有28个相同的上调基因和24个相同的下调基因:上调DEGs的功能集中在离子通道相关通路, 相关信号通路富集于自噬相关通路如mTOR和PI3K/Akt通路、生长因子和整合素相关通路;下调DEGs集中于能量代谢功能和相关信号通路。
      结论     通过分析ADPKD得出的52个DEGs和相关富集信号通路, 可为疾病研究提供潜在的生物标记物和方向;调控ADPKD肾细胞自噬、延缓囊肿进展将可能成为新的研究焦点。

     

    Abstract:
      Objective      The microarray data of autosomal dominant polycystic kidney disease(ADPKD) was downloaded from the Gene Expression Omnibus (GEO) and analyzed to identify the differential expression genes (DEGs) and to explore the possible signal pathways and protein interaction mechanisms in ADPKD by bioinformatics analysis.
      Methods      Two microarray datasets (GSE7869 and GSE35831)of renal cyst tissue of ADPKD patients and dataset of normal controlled tissue were downloaded and screened from GEO database. The DAVID database and Funrich software were used to analyze biological information and signal pathway analysis, and the STRING database was used to analyze protein interaction mechanisms.
      Results      There were 3970 DEGs in GES7869 and 147 DEGs in GSE35831. There were 28 up-regulated genes in the two groups of DEGs and 24 identical down-regulated genes. Up-regulated of DEGs focused on ion channel-related pathways, enriched in autophagy relted pathways, such as mTOR and PI3K/Akt pathways, growth factors and integrin-related pathways, and down-regulated of DEGs focused on energy metabolism and related signaling pathways.
      Conclusions      Analysis of the 52 DEGs and related enrichment signal pathways of the ADPKD could provide potential biomarkers and directions for the future study of ADPKD. Regulation of renal cell autophagy to delay cystic progression might become a new research focus in ADPKD.

     

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