Identification of hub genes and transcription factor-miRNA-mRNA pathways in mice and human renal ischemia-reperfusion injury

• Published in: PeerJ (San Francisco, CA) Vol. 9; p. e12375
• Main Authors: Ke, Peng, Qian, Lin, Zhou, Yi, Feng, Liu, Zhang, Zhentao, Zheng, Chengjie, Chen, Mengnan, Huang, Xinlei, Wu, Xiaodan
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 26.10.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Apoptosis; Bioinformatics; Bioinformatics analysis; Correlation analysis; Datasets; DNA binding proteins; Gene expression; Gene regulation; Genes; Genetic aspects; Genomes; Hub genes; Hypoxia; Ischemia; Ischemia-reperfusion injury; JunB protein; Kidneys; Medical Genetics; Messenger RNA; MicroRNA; miRNA; Nephrology; Online data bases; Ontology; Protein interaction; Protein-protein interactions; Proteins; Regulation; Renal injury; Reperfusion; Reperfusion injury; Software; Statistical analysis; Tissue inhibitor of metalloproteinase 1; Transcription factor; Transcription factors; Urology; Taiwan; Bioinformatics analysis; Renal injury; Hub genes; Ischemia-reperfusion injury; Transcription factor
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.12375

Renal ischemia-reperfusion injury (IRI) is a disease with high incidence rate in kidney related surgery. Micro RNA (miRNA) and transcription factors (TFs) are widely involved in the process of renal IRI through regulation of their target genes. However, the regulatory relationships and functional roles of TFs, miRNAs and mRNAs in the progression of renal IRI are insufficiently understood. The present study aimed to clarify the underlying mechanism of regulatory relationships in renal IRI. Six gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Differently expressed genes (DEGs) and differently expressed miRNAs (DEMs) were identified through RRA integrated analysis of mRNA datasets (GSE39548, GSE87025, GSE52004, GSE71647, and GSE131288) and miRNA datasets (GSE29495). miRDB and TransmiR v2.0 database were applied to predict target genes of miRNA and TFs, respectively. DEGs were applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, followed with construction of protein-protein interaction (PPI) network. Then, the TF-miRNA-mRNA network was constructed. Correlation coefficient and ROC analysis were used to verify regulatory relationship between genes and their diagnostic value in GSE52004. Furthermore, in independent mouse RNA-seq datasets GSE98622, human RNA-seq GSE134386 and , the expression of hub genes and genes from the network were observed and correlation coefficient and ROC analysis were validated. A total of 21 DEMs and 187 DEGs were identified in renal IRI group compared to control group. The results of PPI analysis showed 15 hub genes. The TF-miRNA-mRNA regulatory network was constructed and several important pathways were identified and further verified, including Junb-miR-223-Ranbp3l, Cebpb-miR-223-Ranbp3l, Cebpb-miR-21-Ranbp3l and Cebpb-miR-181b-Bsnd. Four regulatory loops were identified, including Fosl2-miR-155, Fosl2-miR-146a, Cebpb-miR-155 and Mafk-miR-25. The hub genes and genes in the network showed good diagnostic value in mice and human. In this study, we found 15 hub genes and several TF-miRNA-mRNA pathways, which are helpful for understanding the molecular and regulatory mechanisms in renal IRI. Junb-miR-223-Ranbp3l, Cebpb-miR-223-Ranbp3l, Cebpb-miR-21-Ranbp3l and Cebpb-miR-181b-Bsnd were the most important pathways, while Spp1, Fos, Timp1, Tnc, Fosl2 and Junb were the most important hub genes. Fosl2-miR-155, Fosl2-miR-146a, Cebpb-miR-155 and Mafk-miR-25 might be the negative feedback loops in renal IRI.