DKD is one of the most common diabetic complications, as well as the leading cause of chronic kidney disease and end-stage renal disease around the world. Because of the lack of an effective early diagnosis, patients with DKD often lose the chance to benefit from treatment, resulting in poor outcomes. [18] At present, urinary albumin-to-creatinine ratio and eGFR are well-established diagnostic biomarkers of DKD.[19] However, diagnosing DKD also faces challenges associated with both albumin-to-creatinine ratio and eGFR loss are non-specific markers of DKD, and a number of patients with DKD who do not follow the classic pattern of DKD. [8–9] Therefore, researchers are increasingly searching for novel diagnostic biomarkers of DKD.
Recently, mRNAs and microRNAs have emerged as promising biomarkers in DKD [13–14, 20–21]. For example, let-7b-5p and miR-21-5p could serve as biomarkers to predict the risk of ESKD in T1DM, where the elevated expression of the let-7b-5p and miR-21-5p are independent risk factor for ESKD. In particular, let-7c-5p and miR-29a-3p were independently associated with more than a 50% reduction in the risk of rapid progression to ESKD in T1DM.[20] Another study of patients with T1DM without albuminuria revealed that 18 microRNAs were associated with the development of albuminuria and nine of them were used to define a gene signature for microalbuminuria[21]. However, the research mostly uses traditional bioinformatics algorithm, which may lead to excessive data interference and poor reliability of the results. To improve the accuracy of screening molecules, we applied bioinformatics analysis using the system biology method combined with machine learning algorithms to investigate candidate diagnostic marks for DKD.
As far as we know, this is the first retrospective study to identify diagnostic biomarkers in patients with DKD by GEO datasets with WCGNA and machine learning algorithm. We collected one cohort from the GEO datasets and conducted an integrated analysis of the data. A total of 110 DEGs were identified, including 64 upregulated genes and 46 downregulated genes. The turquoise module had the strongest correlation with DKD with gene co-expression network analysis. 38 overlapping genes of DEGs and turquoise modules were found. The results of enrichment analyses indicated that diseases enriched by the overlapping genes were mainly associated with immune regulation, plasma membrane and cell membrane. These findings are in general agreement with the previous finding that an inflammatory response involving leukocytes participates in the pathogenesis of DKD. [22]
The KEEG results demonstrated that the enriched pathways are generally involved in p53 signaling pathway, HIF-1 signaling pathway, JAK − STAT signaling pathway and FoxO signaling pathway. Ma Z et al found that a positive correlation between p53 signaling pathway and renal fibrosis in patients with diabetes.[23] At the same time, they found that p53 microRNA-214/ULK1 axis signaling pathway participates in the occurrence of DKD by inhibiting renal tubular autophagy. Guo W et al. found SIRT1/P53/NRF2 pathway modulates the pathogenesis of DKD. SRT2104, which is a novel, first-in-class, highly selective small-molecule activator of SIRT1 can enhance renal SIRT1 expression and activity, deacetylated P53, and activated NRF2 antioxidant signaling, providing remarkable protection against the DM-induced renal oxidative stress, inflammation, fibrosis, glomerular remodeling and albuminuria in the diabetic mice models.[24–25] Serum HIF-1α may be involved in the DKD process through inflammation, angiogenesis, and endothelial injury.[26] However, the signal pathway is unknown. At present, we found that in mesangial cells, elevated glucose levels induce HIF activity by a hypoxia-independent mechanism. Elevated HIF activity in glomerular cells promotes glomerulosclerosis and albuminuria, and inhibition of HIF protects glomerular integrity. However, tubular HIF activity is suppressed and HIF activation protects mitochondrial function and prevents the development of diabetes-induced tissue hypoxia, tubulointerstitial fibrosis and proteinuria. [27]Therefore, We need further research. The JAK-STAT pathway transmits signals from extracellular ligands, including many cytokines and chemokines as well as growth factors and hormones, directly to the nucleus to induce a variety of cellular responses. [28] Gene and protein expression studies of kidney biopsies from people with early- and late-stage DKD have shown increased activation and expression of the JAK-STAT signaling pathway across the spectrum of DKD.[29] Inhibitors of JAK/STAT pathways are promising therapeutic options to improve the renal outcome of patients with DKD, but appropriate clinical trials are necessary. [30]
Based on two machine-learning algorithms, two diagnostic markers were identified. C-X-C motif chemokine ligand 3(CXCL3) is a member of the CXC subfamily of chemokines produced by inflammatory cells. It mainly recruits and activates a variety of cells expressing CXC chemokine receptor (CXCR) 1 and 2, and participates in the regulation of cell migration, invasion and angiogenesis.[31] At present, the research on CXCL3 mainly focuses on tumor immunity.[32] Blocking the CXCL3 signal transduction pathway can inhibit the pathophysiological processes such as cell migration, invasion, angiogenesis, tumorigenesis and fibrosis, which may become a potential prevention and treatment target for a variety of diseases. We need further research to understand the role of CXCL3 in DKD.
LINC00282 is also known as transmembrane protein 272(TMEM272), which has been predicted to be an integral component of the membrane (https://www.ncbi.nlm.nih.gov/gene/283521). At present, the role of LINC00282 in the process of DKD is not clear. The LINC00282 may become an entry point for future research of on DKD.
The limitations of this study should be acknowledged. First, the study was retrospective; thus, important clinical information was not available. Second, the relatively small number of cases in GSE142153 should be considered a limitation. In addition, the biomarker profiles in the blood cell were obtained from the datasets, and their reproducibility should be further validated. Prospective studies with larger sample sizes should be conducted to validate our conclusions.