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 ... -  《-》

Identification of mitochondria-related genes as diagnostic biomarkers for diabetic nephropathy and their correlation with immune infiltration: New insights from bioinformatics analysis.

Yan Q ，Du Y ，Huang F ，Zhang Q ，Zhan M ，Wu J ，Yan J ，Zhang P ，Lin H ，Han L ，Huang X ... -  《-》

Seven basement membrane-specific expressed genes are considered potential biomarkers for the diagnosis and treatment of diabetic nephropathy.

Diabetic nephropathy (DN) is a diabetes-related chronic vasculitis. DN diminishes kidney function over time and, of course, leads to end stage renal disease in people (ESRD). In spite of the advances in diagnostic and treatment methods for DN, DN continues to impose a significant physical and psychological burden on patients, severely impacting their quality of life, making the hunt for novel therapeutic targets necessary. The Gene Expression Omnibus (GEO) microarray datasets GSE1009, GSE30122, GSE142153, and GSE96804 were downloaded to identify differentially expressed genes (DEGs) in kidney tissues from patients in the DN group and normal controls. These three datasets were examined for genes associated with basement membranes (BMs) with differential gene expression. The target genes were then subjected to gene ontology (GO) annotation and Kyoto Gene and Genome Encyclopedia (KEGG) pathway enrichment analysis. BM-related genes underwent PPI network analysis and screening of the top 10 hub genes, along with immune infiltration analysis and column line graph model development. Finally, we conducted DN therapeutic medication prediction and the creation of something like a miRNA network for genetic markers with BMs. Seven candidate BM-related genes (COL4A1, COL4A2, COL6A2, COL6A3, FN1, ITGQ4, and LAMB1) with acceptable helps the healthcare were discovered. Enrichment analysis of diabetes-related genes event occurred the role of biological processes including extracellular matrix organization, extracellular structural organization, and collagen-containing extracellular matrix, as well as the PI3K-Akt signaling pathway and the AGE-RAGE signaling pathway, in diabetic complications. These genes may also be associated in immune cells and autoimmune activities, such as Macrophages and MHC class I, in order to impact the immune process in DN. In the meanwhile, based on these seven BM-related genes, we discovered that Ginsenoside Rh1 was very significant for drug targeting. This research identified seven BM-related genes as possible diagnostic and therapeutic biomarkers for DN. Analysis of inflammatory infiltration indicated that these genes may be important in inflammatory processes through Macrophages and MHC class I, hence impacting the course and development of DN illness. The development of a correlated column line graph model for it also shown excellent predictive capabilities. In addition, we have found pharmaceuticals, such as Ginsenoside Rh1, that may provide fresh insights into the personalized management of patients with DN.

Gui H ，Chen X ，Ye L ，Ma H ... -  《-》

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis.

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

Xu B ，Wang L ，Zhan H ，Zhao L ，Wang Y ，Shen M ，Xu K ，Li L ，Luo X ，Zhou S ，Tang A ，Liu G ，Song L ，Li Y ... -  《-》

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 ... -  《-》