Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD) in the world. Emerging evidence has shown that urinary mRNAs may serve as early diagnostic and prognostic biomarkers of DKD. In this article, we aimed to first establish a novel bioinformatics-based methodology for analyzing the “urinary kidney-specific mRNAs” and verify their potential clinical utility in DKD. To select candidate mRNAs, a total of 127 Affymetrix microarray datasets of diabetic kidney tissues and other tissues from humans were compiled and analyzed using an integrative bioinformatics approach. Then, the urinary expression of candidate mRNAs in stage 1 study (n = 82) was verified, and the one with best performance moved on to stage 2 study (n = 80) for validation. To avoid potential detection bias, a one-step Taqman PCR assay was developed for quantification of the interested mRNA in stage 2 study. Lastly, the in situ expression of the selected mRNA was further confirmed using fluorescent in situ hybridization (FISH) assay and bioinformatics analysis. Our bioinformatics analysis identified sixteen mRNAs as candidates, of which urinary BBOX1 (uBBOX1) levels were significantly upregulated in the urine of patients with DKD. The expression of uBBOX1 was also increased in normoalbuminuric diabetes subjects, while remained unchanged in patients with urinary tract infection or bladder cancer. Besides, uBBOX1 levels correlated with glycemic control, albuminuria and urinary tubular injury marker levels. Similar results were obtained in stage 2 study. FISH assay further demonstrated that BBOX1 mRNA was predominantly located in renal tubular epithelial cells, while its expression in podocytes and urothelium was weak. Further bioinformatics analysis also suggested that tubular BBOX1 mRNA expression was quite stable in various types of kidney diseases. Our study provided a novel methodology to identify and analyze urinary kidney-specific mRNAs. uBBOX1 might serve as a promising biomarker of DKD. The performance of the selected urinary mRNAs in monitoring disease progression needs further validation.

Objective: This study aims to explore the correlation between ferroptosis and immune microenvironment (IME) in diabetic kidney disease (DKD) to provide a new clue for exploring the underlying molecular mechanisms. Methods: Corresponding RNA data of DKD patients were downloaded from GEO databases. The weighted gene co-expression network analysis (WGCNA) was used to construct the network, and the selected hub genes, then, overlapped with ferroptosis-related genes (FRGs) from FerrDb. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the IME and immune status. Functional analyses including GO, KEGG and GSEA were conducted to elucidate the underlying mechanisms. Results: Two molecular subtypes were identified based on the expression of FRGs. ESTIMATE algorithm revealed that there were significant differences in ESTIMATE score between these two clusters of DKD patients, with no significant difference found in stromal score and immune score. In addition, TIMER algorithm indicated there was a significant difference in the degree of T cell infiltration. The ssGSEA algorithm showed immunity was mainly concentrated in thick ascending limb and distal convoluted tubule in adult kidney. GO, KEGG and GSEA analyses revealed that the differentially expressed genes (DEGs) were mainly enriched in immune and metabolism associated pathways. Conclusion: The ferroptosis may be induced by dysregulation of IME, thereby accelerating the progression of DKD. Our work could be applied to provide a new clue for exploring the underlying molecular mechanisms and sheds novel light on the therapy strategy of DKD.