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Bioinformatic Analysis of Microarray Data of Autosomal Dominant Polycystic Kidney Disease
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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. -
图 1 GSE7869和GSE35831两组芯片的差异表达基因分析
A：GSE7869的DEGs火山图，logFC：表达量倍数，adjP：调整P值；B：GSE35831的DEGs火山图；C：GSE7869和GSE35831共同上调DEGs的维恩图; D：GSE7869和GSE35831共同下调DEGs的维恩图
图 2 GSE7869和GSE35831两组基因芯片DEGs的GO功能富集分析
A、C、E分别为上调DEGs的细胞组成、分子功能和生物学进程前10富集分析结果；B、D、F分别为下调DEGs的细胞组成、分子功能和生物学进程前10富集分析结果
表 1 GSE7869和GSE35831两组基因芯片数据集中表达差异前10位的共同DEGs
基因名 全称 Log FC值 染色体位置 上调基因 SLC7A13 溶质运载蛋白家族7 (阴离子氨基酸转运体)，成员13 7.4734031 8q21.3 PLG 血纤维蛋白溶酶原 6.887972 6q26 CTXN3 皮质素3 6.5219779 5q23.2 PIPOX 哌啶酸氧化酶 6.0353312 17q11.2 SLC22A8 溶质运载蛋白家族22(有机阴离子转运蛋白)，成员8 5.2740998 11q11 FCAMR IgA，IgM高亲和力Fc受体 5.115971 1q32.1 NAT8B N-乙酰转移酶8B 4.7572443 2p13.1 HNF4α 肝细胞核因子4α 4.3174025 20q13.12 TRPM3 瞬态受体电位阳离子通道亚家族M，成员3 4.2564995 9q21.12 TFEC 转录因子EC 4.0514853 7q31.2 下调基因 ALDH1L2 乙醛脱氢酶家族1成员L2 -3.940836 12q23.3 LY96 淋巴细胞抗原96 -3.6708798 8q21.11 SBSPON 生长调节素B&血小板反应蛋白 -3.2250315 8q21.11 C1orf21 染色体1开放阅读框架21 -3.1462389 1q25 FAM43A 序列相似家族43，成员A -3.101968 3q29 TTC39C 三角形四肽重复区域39C -2.9718833 18q11.2 ATP8B2 ATP酶8B2 -2.9020667 1q21.3 EHD2 EH区域包含体2 -2.6773335 19q13.3 ENO2 烯醇酶2 -2.652563 12p13 BCAT1 支链氨基酸转氨酶1 -2.6000445 12p12.1 
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表 2 GSE7869和GSE35831中共同DEGs富集的信号通路
信号通路 基因数(n) 富集基因 上调基因富集通路 SLC介导跨膜转运 3 SLC5A11, SLC22A8, SLC34A3 葡萄糖和其他糖类、胆汁盐和有机酸转运 2 SLC5A11, SLC22A8 小分子跨膜转运 3 SLC5A11, SLC22A8, SLC34A3 整合素家族细胞表面关联 3 PROC, HNF4A, PLG mTOR信号通路 2 HNF4α, PLG Akt介导的I型PI3K信号通路 2 HNF4α, PLG 血管内皮生长因子和血管内皮生长因子受体信号通路 2 HNF4α, PLG 肿瘤坏死因子相关凋亡诱导配体(TRAIL)信号通路 2 HNF4α, PLG 血管内皮生长因子受体1和2介导信号通路 2 HNF4α, PLG 干细胞生长因子受体(c-Met)介导信号通路 2 HNF4α, PLG 下调基因富集通路 磷脂酰肌醇聚糖通路 4 TGFB1I1, PTCH1, FGF2, BCAT1 磷脂酰肌醇聚糖1代谢网络 3 TGFB1I1, FGF2, BCAT1 蛋白多糖介导信号通路 3 TGFB1I1, FGF2, BCAT1 信号转导 2 PTCH1, FGF2 尿激酶型纤溶酶原激活因子介导信号通路 2 TGFB1I1, BCAT1 整合素家族细胞表面关联 2 TGFB1I1, BCAT1 磷脂酰肌醇聚糖3代谢网络 2 TGFB1I1, PTCH1 多配体聚糖4介导信号通路 2 TGFB1I1, FGF2 止血通路 2 EHD2, GUCY1A3
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