Identification of Key Genes and Candidated Pathways in Human Autosomal Dominant Polycystic Kidney Disease by Bioinformatics Analysis
Identification of Key Genes and Candidated Pathways in Human Autosomal Dominant Polycystic Kidney Disease by Bioinformatics Analysis
复制标题
通过生物信息学分析鉴定人类常染色体显性多囊肾病的关键基因和候选通路
DOI：
10.1159/000500458
复制
发表时间：
2019-01-01
影响因子：
2.8
通讯作者：
Mei, Changlin
中科院分区：
文献类型：
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作者：
Liu, Dongmei;Huo, Yongbao;Mei, Changlin
背景/目标：常染色体显性多囊肾病（ADPKD）是肾脏疾病最常见的遗传形式。高通量微阵列分析已应用于阐明与 ADPKD 相关的关键基因和通路。大多数 ADPKD 患者的基因谱数据已上传到公共数据库，但尚未进行彻底分析。本研究整合了 2 个人类微阵列数据集，通过生物信息学分析阐明 ADPKD 中涉及的潜在途径和蛋白质-蛋白质相互作用 (PPI)，从而确定可能的治疗靶点。方法：从NCBI Gene Expression Omnibus中检索并获得ADPKD患者和正常人的肾组织微阵列数据。根据生物信息学分析方案，使用相关网站和软件鉴定差异表达基因（DEG），并阐明富集通路和中心节点基因。通过定量实时聚合酶链反应在多囊肾病和对照肾脏样本之间验证了七个 DEG。结果：对两个原始人类微阵列数据集 GSE7869 和 GSE35831 进行了整合和彻底分析。总共，从 GSE7869 和 GSE35831 中分别提取了 6,422 个和 1,152 个 DEG，其中 561 个 DEG 在数据库之间一致（291 个上调基因和 270 个下调基因）。从 DEG 的 PPI 网络复合体中，从 421 个节点中获得了 34 个中心节点基因。使用 Cytotype MCODE 从 PPI 网络复合体中选择了两个重要模块。大多数已识别的基因涉及蛋白质结合、细胞外区域或空间、血小板脱颗粒、线粒体和代谢途径。结论：通过这种综合生物信息学分析确定的 ADPKD 中的 DEG 和相关富集通路为 ADPKD 的分子机制和潜在的治疗策略提供了见解。具体而言，ADPKD不同阶段的核心蛋白聚糖表达异常可能代表ADPKD的新治疗靶点，而ADPKD代谢和线粒体功能的调控可能成为未来研究的重点。
Background/Aims: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic form of kidney disease. High-throughput microarray analysis has been applied for elucidating key genes and pathways associated with ADPKD. Most genetic profiling data from ADPKD patients have been uploaded to public databases but not thoroughly analyzed. This study integrated 2 human microarray profile datasets to elucidate the potential pathways and protein-protein interactions (PPIs) involved in ADPKD via bioinformatics analysis in order to identify possible therapeutic targets. Methods: The kidney tissue microarray data of ADPKD patients and normal individuals were searched and obtained from NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and enriched pathways and central node genes were elucidated using related websites and software according to bioinformatics analysis protocols. Seven DEGs were validated between polycystic kidney disease and control kidney samples by quantitative real-time polymerase chain reaction. Results: Two original human microarray datasets, GSE7869 and GSE35831, were integrated and thoroughly analyzed. In total, 6,422 and 1,152 DEGs were extracted from GSE7869 and GSE35831, respectively, and of these, 561 DEGs were consistent between the databases (291 upregulated genes and 270 downregulated genes). From 421 nodes, 34 central node genes were obtained from a PPI network complex of DEGs. Two significant modules were selected from the PPI network complex by using Cytotype MCODE. Most of the identified genes are involved in protein binding, extracellular region or space, platelet degranulation, mitochondrion, and metabolic pathways. Conclusions: The DEGs and related enriched pathways in ADPKD identified through this integrated bioinformatics analysis provide insights into the molecular mechanisms of ADPKD and potential therapeutic strategies. Specifically, abnormal decorin expression in different stages of ADPKD may represent a new therapeutic target in ADPKD, and regulation of metabolism and mitochondrial function in ADPKD may become a focus of future research.