Comprehensive analyses of the microRNA–messenger RNA–transcription factor regulatory network in mouse and human renal fibrosis - Deng, Le; Xu, Gaosi; Huang, Qipeng (Frontiers in Genetics, 2022)
Objective The aim of this study was to construct a miRNA-mRNA-transcription factor (TF) regulatory network to explore the potential molecular mechanism, effective biomarker and potential drug in renal fibrosis (RF). Methods Six datasets were downloaded from Gene Expression Omnibus. “Limma” and “DESeq2” packages in R software and GEO2R were applied to identify the differentially expressed miRNAs and mRNA (DEmiRNAs, DEmRNAs). The determination and verification of DEmiRNAs and related genes were performed through the integrated analysis of datasets from five mouse 7 days of unilateral ureteral obstruction datasets and one human chronic kidney disease dataset and The Human Protein Atlas. Target mRNAs of DEmiRNAs and transcription factors (TFs) were predicted by online prediction databases and iRegulon plugin in Cytoscape, respectively. Construction of protein-protein interaction network was undertaken using STRING, Cytoscape v3.9.1 and cytoNCA. Functional enrichment analysis was performed utilizing DIANA-miRPath v3.0 and R package “clusterProfiler”. A miRNA-mRNA-TF network was established with Cytoscape. Receiver operating characteristic (ROC) analysis was used to explore the diagnostic value of the key hub genes. Finally, Comparative Toxicogenomics Database (CTD) and Drug-Gene Interaction database (DGIdb) were applied to identify potential drugs. Results 4 DEmiRNAs and 11 hub genes were determined and confirmed in the mouse validation datasets, of which 2 key hub genes (Bckdha and Vegfa ) were verified in the human validation dataset and the expressions of Bckdha and Vegfa were further comfirmed by The Human Protein Atlas. Moreover, Bckdha and Vegfa were also predicted by paired miRNA respectively in human as in mouse. 6 TFs were predicted to regulate Bckdha and Vegfa across mouse and human, and a miRNA-mRNA-TF regulatory network was built. Moreover, ROC analysis showed that area under the curve value of Vegfa was 0.825 (P = 0.002). In addition, enalapril was identifed to target Vegfa in RF. Conclusions Pax2, Pax5, Sp1, Sp2, Sp3 and Sp4 together with Bckdha-dependent miR-125a-3p/Vegfa-dependent miR-199a-5p formed a co-regulatory network enabling Bckdha/Vegfa to be tightly involved in the underlying pathogenesis of RF. Vegfa might be used as an potential novel diagnostic marker and may be targeted by enalapril for RF therapy.