School of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Affiliated Hospital of Shandong University of Traditional Chinese Medicine Nephrology, Jinan, China.
J Healthc Eng. 2021 Nov 16;2021:5331948. doi: 10.1155/2021/5331948. eCollection 2021.
MicroRNAs (miRNAs) are confirmed to participate in occurrence, development, and prevention of membranous nephropathy (MN), but their mechanism of action is unclear.
With the GEO database and the use of bioinformatics, miRNA-mRNA regulatory network genes relevant to MN were explored and their potential mechanism of action was explained.
The MN-related miRNA chip data set (GSE51674) and mRNA chip data set (GSE108109) were downloaded from the GEO database. Differential analysis was performed using the GEO2R online tool. TargetScan, miRTarBase, and StarBase databases were used to predict potential downstream target genes regulated by differentially expressed miRNAs, and the intersection with differential genes were taken to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair was clarified and Cytoscape was used to construct a miRNA-mRNA regulatory network. WebGestalt was used to conduct enrichment analysis of the biological process of differential mRNAs in the regulatory network; FunRich analyzes the differential mRNA pathways in the miRNA-mRNA regulatory network. And the STRING database was used to construct a PPI network for candidate target genes, and Cytoscape visually analyzes the PPI network.
Experiments were conducted to screen differentially expressed miRNAs and mRNAs. There were 30 differentially expressed miRNAs, including 22 upregulated and 8 downregulated; and 1267 differentially expressed mRNAs, including 536 upregulated and 731 downregulated. Using TargetScan, miRTarBase, and StarBase databases to predict the downstream targets of differentially expressed miRNAs, 2957 downstream target genes coexisting in the 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 175 candidate target genes. Finally, 36 miRNA-mRNA relationship pairs comprising 10 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out, and the regulatory network was constructed. Further analysis revealed that the miRNA regulatory network genes may be involved in the development of membranous nephropathy by mTOR, PDGFR-, LKB1, and VEGF/VEGFR signaling pathways.
The miRNA regulatory network genes may participate in the regulation of podocyte autophagy, lipid metabolism, and renal fibrosis through mTOR, PDGFR-, LKB1, and VEGF/VEGFR signaling pathways, thereby affecting the occurrence and development of membranous nephropathy.
微小 RNA（miRNAs）已被证实参与膜性肾病（MN）的发生、发展和防治，但作用机制尚不清楚。
利用 GEO 数据库，通过生物信息学方法，探讨与 MN 相关的 miRNA-mRNA 调控网络基因，并阐明其潜在作用机制。
从 GEO 数据库中下载 MN 相关 miRNA 芯片数据集（GSE51674）和 mRNA 芯片数据集（GSE108109）。使用 GEO2R 在线工具进行差异分析。利用 TargetScan、miRTarBase 和 StarBase 数据库预测差异表达 miRNA 调控的潜在下游靶基因，并与差异基因取交集，获得候选靶基因。根据 miRNA 和 mRNA 之间的调控关系，阐明 miRNA-mRNA 调控网络的 miRNA-mRNA 关系对，并利用 Cytoscape 构建 miRNA-mRNA 调控网络。利用 WebGestalt 对调控网络中差异 mRNA 的生物学过程进行富集分析；利用 FunRich 分析 miRNA-mRNA 调控网络中差异 mRNA 通路。利用 STRING 数据库构建候选靶基因的 PPI 网络，并利用 Cytoscape 进行可视化分析。
实验筛选出差异表达的 miRNAs 和 mRNAs，共筛选出 30 个差异表达 miRNA，包括 22 个上调和 8 个下调；1267 个差异表达 mRNAs，包括 536 个上调和 731 个下调。利用 TargetScan、miRTarBase 和 StarBase 数据库预测差异表达 miRNAs 的下游靶基因，预测出存在于 3 个数据库中的 2957 个下游靶基因与差异表达 mRNAs 取交集，获得 175 个候选靶基因。最终筛选出包括 10 个差异表达 miRNA 和 27 个差异表达 mRNAs 的 36 个 miRNA-mRNA 关系对，构建调控网络。进一步分析发现，miRNA 调控网络基因可能通过 mTOR、PDGFR-、LKB1 和 VEGF/VEGFR 信号通路参与膜性肾病的发生发展。
miRNA 调控网络基因可能通过 mTOR、PDGFR-、LKB1 和 VEGF/VEGFR 信号通路参与调控足细胞自噬、脂质代谢和肾纤维化，从而影响膜性肾病的发生发展。
