Identifying Potential Diagnostic Genes for Diabetic Nephropathy Based on Hypoxia and Immune Status

Abstract

Background The prognosis of diabetic nephropathy is poor, and early diagnosis of diabetic nephropathy is challenging. Fortunately, searching for DN-specific markers based on machine algorithms can facilitate diagnosis. Methods xCell model and CIBERSORT algorithm were used to analyze the relationship between immune cells and DN, and WGCNA analysis was used to evaluate the regulatory relationship between hypoxia gene and DN-related immune cells. Lasso regression and ROC regression were used to detect the ability of core genes to diagnose DN, the PPI network of core genes with high diagnostic ability was constructed, and the interaction between core genes was discussed. Results There were 519 differentially expressed genes in renal tubules and 493 differentially expressed genes in glomeruli. Immune and hypoxia responses are involved in the regulation of renal glomerulus and renal tubules. We found that there are 16 hypoxia-related genes involved in the regulation of hypoxia response. Seventeen hypoxia-related genes in renal tubules are involved in regulating hypoxia response on the proteasome signal pathway. Lasso and ROC regression were used to screen anoxic core genes. Further, we found that TGFBR3, APOLD1, CPEB1, and KDR are important in diagnosing DN glomerulopathy, respectively, PSMB8, PSMB9, RHOA, VCAM1, and CDKN1B, which have high specificity for renal tubulopathy in DN. Conclusion Hypoxia and immune reactions are involved in the progression of DN. T cells are the central immune response cells. TGFBR3, APOLD1, CPEB1, and KDR have higher diagnostic accuracy in the diagnosis of DN. PSMB8, PSMB9, RHOA, VCAM1, and CDKN1B have higher diagnostic accuracy in DN diagnosis.

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

“…First, due to the small number of data sets integrated, only data sets related to DN genes were obtained from NCBI database, so other undiscovered DEGs still need to be explored based on multiple databases. Second, although identified hub genes have been reported to be closely related to the regulation of immune responses, there is still little evidence that they can clearly regulate immune responses in the DN disease process ( Li et al, 2021 ). The observed high expression of immune-related genes in DN may be due to the enrichment of immune cells in DN tissue.…”

Multi-database analysis showed that genes associated with inflammatory response are important for prognosis prediction of renal clear cell carcinoma

“…[11] Studies have con rmed the important role of APOLD1 in in ammatory response-related diseases, such as diabetic nephropathy and osteoarthritis. [11,12] Similarly, this gene can predict the prognosis of patients with clear cell carcinoma of the kidney. [13] CPA3 is associated with histone deacetylase activation.…”

Schisandrin A from Schisandra chinensis Attenuates Ferroptosis and NLRP3 Inflammasome-Mediated Pyroptosis in Diabetic Nephropathy through Mitochondrial Damage by AdipoR1 Ubiquitination

“…With the adult diabetes prevalence rate, China has become one of the countries with the fastest growth rate of diabetes prevalence [ 22 ]. Since type 2 diabetes is an important cause of end-stage renal disease, the early diagnosis and treatment of type 2 diabetes with renal damage have been a hotspot for medical workers at home and abroad [ 23 , 24 ]. Dong et al validated that Schisandrin A protected high glucose-induced cell injury [ 25 ].…”