Urinary mRNA Signatures as Predictors of Renal Function Decline in Patients With Biopsy-Proven Diabetic Kidney Disease

NET-Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury

Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear. Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD's tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury. Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury. Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD.

Identification and validation of voltage-dependent anion channel 1-related genes and immune cell infiltration in diabetic nephropathy

AIMS/INTRODUCTION

This study investigated the roles of voltage-dependent anion channel 1-related differentially expressed genes (VRDEGs) in diabetic nephropathy (DN).

MATERIALS AND METHODS

We downloaded two datasets from patients with DN, namely, GSE30122 and GSE30529, from the Gene Expression Omnibus database. VRDEGs associated with DN were obtained from the intersection of voltage-dependent anion channel 1-related genes from the GeneCards database, and differentially expressed genes were screened according to group (DN/healthy) in the two datasets. The enriched pathways of the VRDEGs were analyzed. Hub genes were selected using a protein-protein interaction network, and their predictive value was verified through receiver operating characteristic curve analysis. The CIBERSORTx software examined hub genes and immune cell infiltration associations. The protein expression of hub genes was verified through immunohistochemistry in 16-week-old db/db mice for experimentation as a model of type 2 DN. Finally, potential drugs targeting hub genes that inhibit DN development were identified.

RESULTS

A total of 57 VRDEGs were identified. The two datasets showed high expression of the PI3K, Notch, transforming growth factor-β, interleukin-10 and interleukin-17 pathways in DN. Five hub genes (ITGAM, B2M, LYZ, C3 and CASP1) associated with DN were identified and verified. Immunohistochemistry showed that the five hub genes were highly expressed in db/db mice, compared with db/m mice. The infiltration of immune cells was significantly correlated with the five hub genes.

CONCLUSIONS

Five hub genes were significantly correlated with immune cell infiltration and might be crucial to DN development. This study provides insight into the mechanisms involved in the pathogenesis of DN.

Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning

Backgrounds

Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown.

Methods

Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap.

Results

Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers.

Conclusions

Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN.