Identification of Lumican and Fibromodulin as Hub Genes Associated with Accumulation of Extracellular Matrix in Diabetic Nephropathy.

Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e-15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN.

Feng S ，Gao Y ，Yin D ，Lv L ，Wen Y ，Li Z ，Wang B ，Wu M ，Liu B ... -  《-》

Identification of Lipotoxicity-Related Biomarkers in Diabetic Nephropathy Based on Bioinformatic Analysis.

Objective: This study is aimed at investigating diagnostic biomarkers associated with lipotoxicity and the molecular mechanisms underlying diabetic nephropathy (DN). Methods: The GSE96804 dataset from the Gene Expression Omnibus (GEO) database was utilized to identify differentially expressed genes (DEGs) in DN patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the DEGs. A protein-protein interaction (PPI) network was established to identify key genes linked to lipotoxicity in DN. Immune infiltration analysis was employed to identify immune cells with differential expression in DN and to assess the correlation between these immune cells and lipotoxicity-related hub genes. The findings were validated using the external dataset GSE104954. ROC analysis was performed to assess the diagnostic performance of the hub genes. The Gene set enrichment analysis (GSEA) enrichment method was utilized to analyze the key genes associated with lipotoxicity as mentioned above. Result: In this study, a total of 544 DEGs were identified. Among them, extracellular matrix (ECM), fatty acid metabolism, AGE-RAGE, and PI3K-Akt signaling pathways were significantly enriched. Combining the PPI network and lipotoxicity-related genes (LRGS), LUM and ALB were identified as lipotoxicity-related diagnostic biomarkers for DN. ROC analysis showed that the AUC values for LUM and ALB were 0.882 and 0.885, respectively. The AUC values for LUM and ALB validated in external datasets were 0.98 and 0.82, respectively. Immune infiltration analysis revealed significant changes in various immune cells during disease progression. Macrophages M2, mast cells activated, and neutrophils were significantly associated with all lipotoxicity-related hub genes. These key genes were enriched in fatty acid metabolism and extracellular matrix-related pathways. Conclusion: The identified lipotoxicity-related hub genes provide a deeper understanding of the development mechanisms of DN, potentially offering new theoretical foundations for the development of diagnostic biomarkers and therapeutic targets related to lipotoxicity in DN.

Nie H ，Yang H ，Cheng L ，Yu J ... -  《-》

Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm.

Gholaminejad A ，Fathalipour M ，Roointan A 《BMC Nephrology》

Identification of PDK4 as Hub Gene for Diabetic Nephropathy Using Co-Expression Network Analysis.

Diabetic nephropathy (DN) is related to type 1 and type 2 diabetes. They are the leading cause of end-stage renal disease, but the underling specific pathogenesis of DN is not yet clear. Our study was conducted to explore how DN changed the transcriptome profiles in the kidney. The gene expression profile of microdissected glomeruli of 41 type 2 DN patients and 20 healthy controls were included. The sample dataset GSE96804 was obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed in R with the limma package and the important modules were found by weighted gene co-expression network analysis (WGCNA) clustering. The modules were then analyzed based on Gene Ontology (GO) gene set enrichment analysis, and the hub genes were found out. We next validated the hub gene, PDK4, in a cell model of DN. We also constructed the PDK4-related PPI network to investigate the correlation between PDK4 expression and other genes. Heatmap and volcano map were drawn to illustrate the mRNA expression profile of 1,204 DEGs in both samples of DN patients and the control group. Using WGCNA, we selected the blue module in which genes showed the strongest correlation with the phenotype and the smallest p value. We also identified PDK4 as a hub gene. PDK4 expression was upregulated in human diabetic kidney tissue. Moreover, PDK4 was speculated to play a role in glomerular basement membrane development and kidney development according to the enrichment of functions and signaling pathways. Furthermore, PDK4 and two key genes GSTA2 and G6PC protein expression were verified highly expressed in the cell model of DN. During the pathogenesis of DN, many genes may change expression in a coordinated manner. The discovery of PDK4 as key gene using WGCNA is of great significance for the development of new treatment strategies to block the development of DN.

Han Y ，Jin L ，Wang L ，Wei L ，Tu C ... -  《-》

Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy.

Diabetic nephropathy (DN) is among the leading causes of end-stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co-expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases. The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre-processing, weighted gene co-expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis-based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments. A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN. Weighted gene co-expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN.

Wang Z ，Chen X ，Li C ，Tang W ... -  《-》