School of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Shandong 250014, China.
Department of Geriatrics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Shandong, China.
J Healthc Eng. 2021 Nov 18;2021:8161701. doi: 10.1155/2021/8161701. eCollection 2021.
MicroRNA (miRNA) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (DN), but its mechanism of action is still unclear.
With the help of the GEO database, bioinformatics methods are used to explore the miRNA-mRNA regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action.
The DN-related miRNA microarray dataset (GSE51674) and mRNA expression dataset (GSE30122) are downloaded through the GEO database, online analysis tool GEO2R is used for data differential expression analysis, TargetScan, miRTarBase, and miRDB databases are used to predict potential downstream target genes regulated by differentially expressed miRNAs, and intersection with differential genes is used to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair is clarified, and the miRNA-mRNA regulatory network is constructed using Cytoscape. DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. By GeneMANIA prediction of miRNA target genes and coexpressed genes, the protein interaction network is constructed. s. A total of 67 differentially expressed miRNAs were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed mRNAs were screened, of which 93 were upregulated and 355 were downregulated. Using TargetScan, miRTarBase, and miRDB databases to predict downstream targets of differentially expressed miRNAs, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 96 candidate target genes. Finally, 44 miRNA-mRNA relationship pairs consisting of 12 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out; further analysis showed that miRNA regulatory network genes may participate in the occurrence and development of diabetic nephropathy through PI3K/Akt, ECM-receptor interaction pathway, and RAS signaling pathway.
微小 RNA（miRNA）已被证实参与糖尿病肾病（DN）的发生、发展和防治，但作用机制尚不清楚。
借助 GEO 数据库，采用生物信息学方法挖掘与糖尿病肾病相关的 miRNA-mRNA 调控关系对，并对其潜在作用机制进行阐释。
通过 GEO 数据库下载 DN 相关 miRNA 芯片数据集（GSE51674）和 mRNA 表达数据集（GSE30122），运用在线分析工具 GEO2R 进行数据差异表达分析，利用 TargetScan、miRTarBase、miRDB 数据库预测差异表达 miRNA 调控的潜在下游靶基因，与差异基因取交集获得候选靶基因。根据 miRNA 和 mRNA 之间的调控关系，厘清 miRNA-mRNA 关系对，运用 Cytoscape 构建 miRNA-mRNA 调控网络。利用 DAVID 对候选靶基因进行 GO 功能富集分析和 KEGG 通路分析。通过 GeneMANIA 预测 miRNA 靶基因和共表达基因，构建蛋白质相互作用网络。实验共筛选出 67 个差异表达 miRNA，其中上调 42 个，下调 25 个；筛选出差异表达 mRNAs 共 448 个，其中上调 93 个，下调 355 个。运用 TargetScan、miRTarBase、miRDB 数据库预测差异表达 miRNA 的下游靶基因，共预测到 3 个数据库中存在交集的下游靶基因 2283 个，与差异表达 mRNAs 取交集获得 96 个候选靶基因。最终筛选出包含 12 个差异表达 miRNA 和 27 个差异表达 mRNAs 的 44 个 miRNA-mRNA 关系对；进一步分析发现，miRNA 调控网络基因可能通过 PI3K/Akt、ECM-受体相互作用通路、RAS 信号通路参与糖尿病肾病的发生发展。
