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Updated: Aug 17, 2025
Using 2-Photon Microscopy to Quantify the Effects of Chronic Unilateral Ureteral Obstruction on Glomerular Processes
Published on: March 4, 2022
Novel targets in renal fibrosis based on bioinformatic analysis
Yuan Yuan1, Xi Xiong2, Lili Li3
1Department of Urology, Wuhan Third Hospital and Tongren Hospital of Wuhan University, Wuhan, China.
Abstract:
Background: Renal fibrosis is a widely used pathological indicator of progressive chronic kidney disease (CKD), and renal fibrosis mediates most progressive renal diseases as a final pathway. Nevertheless, the key genes related to the host response are still unclear. In this study, the potential gene network, signaling pathways, and key genes under unilateral ureteral obstruction (UUO) model in mouse kidneys were investigated by integrating two transcriptional data profiles. Methods: The mice were exposed to UUO surgery in two independent experiments. After 7 days, two datasets were sequenced from mice kidney tissues, respectively, and the transcriptome data were analyzed to identify the differentially expressed genes (DEGs). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were executed. A Protein-Protein Interaction (PPI) network was constructed based on an online database STRING. Additionally, hub genes were identified and shown, and their expression levels were investigated in a public dataset and confirmed by quantitative real time-PCR (qRT-PCR) in vivo. Results: A total of 537 DEGs were shared by the two datasets. GO and the KEGG analysis showed that DEGs were typically enriched in seven pathways. Specifically, five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified by performing the 12 scoring methods in cytoHubba, and the receiver operating characteristic (ROC) curve indicated that the hub genes could be served as biomarkers. Conclusion: A gene network reflecting the transcriptome signature in CKD was established. The five hub genes identified in this study are potentially useful for the treatment and/or diagnosis CKD as biomarkers.
Insights
Researchers identified five key genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) that could serve as biomarkers for chronic kidney disease (CKD) progression. This study establishes a gene network for understanding CKD transcriptome signatures.
Area of Science:
- Genomics and Bioinformatics
- Renal Pathophysiology
Background:
- Renal fibrosis is a critical indicator of progressive chronic kidney disease (CKD), representing a common final pathway for many renal diseases.
- The specific genes involved in the host response during renal fibrosis remain largely undefined.
Purpose of the Study:
- To investigate the gene network, signaling pathways, and key genes associated with renal fibrosis using a unilateral ureteral obstruction (UUO) mouse model.
- To identify potential diagnostic and therapeutic biomarkers for CKD.
Main Methods:
- Integration of two transcriptome datasets from mouse kidneys following UUO surgery.
- Identification of differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
- Construction of a Protein-Protein Interaction (PPI) network to identify hub genes, with validation using public datasets and quantitative real-time PCR (qRT-PCR).
Main Results:
- A total of 537 DEGs were common across both datasets.
- Enrichment analysis revealed significant involvement of DEGs in seven key pathways.
- Five hub genes (Bmp1, CD74, Fcer1g, Icam1, H2-Eb1) were identified and demonstrated potential as biomarkers via ROC curve analysis.
Conclusions:
- A comprehensive gene network reflecting the transcriptome signature of CKD was successfully established.
- The identified five hub genes hold significant potential as biomarkers for the diagnosis and treatment of CKD.
