Abstract

Introduction

   Diabetic kidney disease (DKD) is a long-term complication of diabetes
   and causes renal microvascular disease. It is also one of the main
   causes of end-stage renal disease (ESRD), which has a complex
   pathophysiological process. Timely prevention and treatment are of
   great significance for delaying DKD. This study aimed to use
   bioinformatics analysis to find key diagnostic markers that could be
   possible therapeutic targets for DKD.

Methods

   We downloaded DKD datasets from the Gene Expression Omnibus (GEO)
   database. Overexpression enrichment analysis (ORA) was used to explore
   the underlying biological processes in DKD. Algorithms such as WGCNA,
   LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The
   reliability and practicability of the the diagnostic model were
   evaluated by the calibration curve, ROC curve, and DCA curve. GSEA
   analysis and correlation analysis were used to explore the biological
   processes and significance of candidate markers. Finally, we
   constructed a mouse model of DKD and diabetes mellitus (DM), and we
   further verified the reliability of the markers through experiments
   such as PCR, immunohistochemistry, renal pathological staining, and
   ELISA.

Results

   Biological processes, such as immune activation, T-cell activation, and
   cell adhesion were found to be enriched in DKD. Based on differentially
   expressed oxidative stress and inflammatory response-related genes
   (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four
   potential diagnostic markers for DKD, including tenascin C,
   peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin
   (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using
   multiple bioinformatics analyses. Further enrichment analysis found
   that four diagnostic markers were closely related to various immune
   cells and played an important role in the immune microenvironment of
   DKD. In addition, the results of the mouse experiment were consistent
   with the bioinformatics analysis, further confirming the reliability of
   the four markers.

Conclusion

   In conclusion, we identified four reliable and potential diagnostic
   markers through a comprehensive and systematic bioinformatics analysis
   and experimental validation, which could serve as potential therapeutic
   targets for DKD. We performed a preliminary examination of the
   biological processes involved in DKD pathogenesis and provide a novel
   idea for DKD diagnosis and treatment.

   Keywords: diabetic kidney disease, biomarker, diagnostic model, immune,
   bioinformatic analysis

Introduction

   Diabetes kidney disease (DKD) is a chronic kidney disease caused by
   diabetes. About 40% of type 2 diabetes patients and 30% of type 1
   diabetes patients present with DKD ([49]1, [50]2). With the increasing
   prevalence of diabetes, the number of DKD patients has also increased
   ([51]3, [52]4). DKD patients present different forms of kidney damage,
   which is characterized by continuous increase of albuminuria excretion
   and/or a progressive decrease in glomerular filtration rate (GFR),
   eventually developing into end-stage renal disease (ESRD) ([53]5). DKD
   is the main cause of ESRD, and about 30% to 50% of worldwide ESRD is
   caused by DKD ([54]6). Therefore, it is urgent to explore early
   effective diagnosis and intervention targets for exploring new
   diagnosis and treatment strategies to improve the clinical DKD outcome.

   The pathogenesis of DKD is complex and multifactorial. Generally, DKD
   is mainly caused by hemodynamic changes and metabolic disturbances
   ([55]7). These changes subsequently lead to activation of the
   renin-angiotensin-aldosterone system (RAAS) ([56]8), increases in
   metabolites and pro-inflammatory factors, and dysregulation of many
   intracellular signaling cascades associated with oxidative stress
   ([57]9–[58]11). In the state of diabetes, on one hand, the
   self-oxidation of glucose causes mitochondrial overload and excessive
   production of reactive oxygen species (ROS). On the other hand, the
   body’s antioxidant capacity decreases, and the amount of intracellular
   antioxidant (nicotinamide adenine dinucleotide phosphate [NADPH]) is
   insufficient ([59]12), resulting in an imbalance between oxidants and
   antioxidants. In addition, oxidative stress is also closely related to
   inflammatory cells, which often coexist and activate each other.
   Excessive oxidative stress and inflammatory responses lead to damage to
   the renal interstitium, glomeruli, and renal podocytes, thereby
   impairing renal function. Therefore, finding diagnostic and therapeutic
   targets for oxidative stress and inflammatory response is expected to
   block the process of renal injury in DKD and restore renal function.

   With the popularization of gene chips and high-throughput sequencing,
   many disease databases have gradually been improved, and more and more
   effective data can be used to reveal the pathogenesis of diseases and
   new therapeutic targets. For example, Ma used a bioinformatic approach
   to analyze gene expression profiles and underlying functional networks
   in cardiac tissue from patients with dilated cardiomyopathy ([60]13).
   Huang analyzed the correlation of serum 25-hydroxyvitamin D levels in
   the progression of proteinuria in DKD and its underlying mechanisms
   ([61]14). Yang explored seven immune-related genes that can predict the
   progression of atherosclerotic plaque based on machine learning
   ([62]15). However, existing studies have some deficits, such as
   analysis based on a single dataset, limited number of patients, and no
   multi-faceted validation of bioinformatics methods, which affects
   prediction capability or reliability. This study integrated DKD
   datasets from different sources, used a variety of biological
   information methods to screen diagnostic markers related to oxidative
   stress and inflammatory response in DKD, and thoroughly examined the
   biological functions and potential mechanisms of diagnostic markers.
   This discovery may provide a promising direction for clarifying the
   diagnosis and pathogenesis of DKD.

Materials and methods

Data sources and processing

   DKD gene expression profiling data were downloaded from the Gene
   Expression Omnibus (GEO) database
   ([63]https://www.ncbi.nlm.nih.gov/geo/), including seven datasets,
   [64]GSE111154, [65]GSE142025, [66]GSE162830, [67]GSE163603,
   [68]GSE96804, [69]GSE1009, and [70]GSE30122. [71]Table 1 presents more
   details concerning the above datasets. Excluding the samples irrelevant
   to this study, 214 samples were finally obtained, including 101 normal
   samples and 113 DKD samples. The “sva” R package was applied for
   removing batch effects from different datasets ([72]16). A Principal
   component analysis (PCA) was utilized to assess the effect of batch
   effect removal and visualize the distribution of DKD and normal patient
   samples. Subsequently, we obtained 458 oxidative stress-related genes
   from the Gene Ontology (GO) knowledgebase
   ([73]http://geneontology.org/) and 200 inflammatory response related
   genes from MsigDB (HALLMARK_INFLAMMATORY_RESPONSE)
   ([74]http://www.broad.mit.edu/gsea/msigdb/) as shown in
   [75]Supplementary Table 1 .

Table 1.

   Details of the datasets included in this study.
   Datasets Platforms Organism DKD Normal References Status