Identification of metabolic reprogramming-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics

Abstract

Background: Diabetic nephropathy (DN) is a serious complication of diabetes mellitus, marked by progressive renal damage. Recent evidence indicates that metabolic reprogramming is crucial to DN pathogenesis, yet its underlying mechanisms are not well understood. This study aimed to examine how metabolic reprogramming-related genes (MRRGs) are differentially expressed and to explore their potential mechanisms in the development of DN.

Methods: We analyzed the datasets GSE30528 and GSE96804 from the Gene Expression Omnibus (GEO), comprising 50 DN samples and 33 controls. MRRGs were sourced from GeneCards and PubMed. Data preprocessing included batch effect correction using the R package sva, followed by normalization and differential expression analysis with limma (|logFC|> 0.5, adj.p < 0.05). Functional enrichment analyses (GO, KEGG, GSEA) were performed using clusterProfiler. Protein-protein interaction (PPI) networks were constructed via STRING, identifying hub genes through CytoHubba. Regulatory networks (mRNA-TF, mRNA-miRNA) were derived from ChIPBase and StarBase. Validation of hub genes and ROC analysis assessed diagnostic performance. ssGSEA quantified immune cell infiltration.

Results: Our analysis identified 708 differentially expressed genes (DEGs), including 119 metabolic reprogramming-related DEGs (MRRDEGs). Enrichment analyses revealed significant roles for MRRDEGs in processes such as wound healing and pathways like MAPK signaling. The PPI network identified nine hub genes: FN1, CD44, KDR, EGF, HSPG2, HGF, FGF9, IGF1, and ALB, which exhibited high diagnostic accuracy (AUC 0.7 to 0.9). Notably, FN1 and CD44 showed significant association with renal fibrosis and could serve as potential biomarkers for early diagnosis and therapeutic targets in DN. Immune infiltration analysis showed notable differences in immune cell composition between DN and control samples.

Conclusion: This study identifies hub genes such as FN1 and CD44, with potential diagnostic value in DN. It also reveals immune cell infiltration differences between DN patients and controls, offering insights into disease progression and potential therapeutic targets.

Keywords: Bioinformatics; Biomarker; Diabetic nephropathy; Metabolic reprogramming; Metabolic reprogramming-related genes.

Fig.1

Flow chart for the comprehensive analysis of MRRDEGs. DN Diabetic nephropathy, GSEA Gene Set Enrichment Analysis, DEGs Differentially Expressed Genes, MRRGs Metabolic Reprogramming Related Genes, MRRDEGs Metabolic Reprogramming-Related Differentially Expressed Genes, GO Gene Ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, PPI Protein–Protein Interaction, ROC Receiver Operating Characteristic, TF Transcription Factor, ssGSEA single-sample Gene-Set Enrichment Analysis

Fig. 2

Batch effects removal of GSE30528 and GSE96804. A Distribution boxplots of combined dataset before normalization. B Distribution boxplots of combined dataset after normalization. C PCA plot of combined dataset before normalization. D PCA plot of Combined Dataset after normalization. In the figures, the DN dataset GSE30528 is represented in orange, while GSE96804 is shown in green. PCA principal component analysis

Fig. 3

Differential gene expression analysis. A Volcano plot depicting DEGs analysis between DN and Control in Combined Dataset. B Venn diagram illustrating the overlap between DEGs and MRRGs. C Heatmap displaying the top 20 MRRDEGs. In the heatmap, orange color indicates control samples, grey denotes DN samples, red represents high expression, blue indicates lower expression. DN diabetic nephropathy, DEGs differentially expressed genes, MRRGs metabolic reprogramming related genes, MRRDEGs metabolic reprogramming—related differentially expressed genes

Fig. 4

GO and KEGG Enrichment Analysis for MRRDEGs. A Bar graphs depicting GO and KEGG enrichment analysis results of MRRDEGs. The x-axis represents GO terms and KEGG terms. B–D Network diagram illustrating GO enrichment analysis of BP, CC and MF. E Network diagram illustrating KEGG pathway enrichment analysis for MRRDEGs. The screening criteria for GO and KEGG enrichment analysis were adj.p < 0.05 and FDR < 0.25, and the p value correction method was BH. The orange nodes represent items, the green nodes represent molecules, and the lines represent the relationship between items and molecules. MRRDEGs metabolic reprogramming—related differentially expressed genes, GO gene ontology, KEGG Kyoto Encyclopedia of Genes and Genomes, BP biological process, CC cellular component, MF molecular function, FDR false discovery rate, BH Benjamini-Hochberg

Fig. 5

GSEA for combined dataset. A The bubble plot illustrated the GSEA results for four biological functions in the Combined Dataset. B–E GSEA demonstrated significant enrichment of all genes in the Inflammatory Response Pathway (B), P130cas Linkage to MAPK Signaling for Integrins (C), Quercetin and NF-κB Ap1 Induced Apoptosis (D), and Fatty Acid Metabolism (E). Bubble size represented the number of enriched genes, while bubble color indicated the NES; warmer colors denoted higher NES values (red) and cooler colors denoted lower NES values (blue). GSEA criteria include adj.p < 0.05 and FDR < 0.25, with p value adjusted using the BH method. GSEA Gene Set Enrichment Analysis, FDR false discovery rate, BH Benjamini-Hochberg

Fig. 6

PPI network and Hub genes analysis. A the PPI network of MRRDEGs, computed using the STRING database. B–F The PPI Network illustrated the top 20 MRRDEGs associated with metabolic reprogramming, identified using five algorithms from the CytoHubba plugin. including the Closeness (B), Degree (C), EPC (D), MCC (E) and MNC (F). G the Venn diagram depicting the intersection of the top 20 MRRDEGs identified by above five algorithms of the CytoHubba plugin. PPI Protein—Protein Interaction, MRRDEGs Metabolic Reprogramming-Related Differentially Expressed Genes, EPC Edge Percolated Component, MCC Maximal Clique Centrality, MNC Maximum Neighborhood Component

Fig. 7

Regulatory Network of Hub Genes. A mRNA-miRNA Regulation Network of Hub Genes. B mRNA-TF Regulatory Network of Hub Genes. mRNAs are shown in blue, miRNAs in orange, and TFs in green. TF transcription factor

Fig. 8

Differential expression validation and ROC curve analysis. A Group comparison plots of Hub Genes in DN and Control from the Combined Dataset. B ROC curves for Hub Genes FN1, CD44, KDR in the Combined Dataset. C ROC curves for Hub Genes EGF, HSPG2, HGF in the Combined Dataset. D ROC curves for Hub Genes FGF9, IGF1, ALB in the Combined Dataset. *** indicates p value < 0.001, indicating statistical significance. In the comparison plots, orange indicates Control samples, while gray indicates DN samples. AUC ranges from 0.7 to 0.9 indicate moderate accuracy. DN diabetic nephropathy, ROC Receiver Operating Characteristic, AUC Area Under the Curve, TPR True Positive Rate, FPR False Positive Rate

Fig. 9

Immune Infiltration Analysis by ssGSEA Algorithm. A Group comparison plots of immune cell integration in samples from DN and Control groups within the Combined Dataset. B Heatmap depicting the correlation of immune cell infiltration abundances within the Combined Dataset. C Bubble plot illustrating the correlation between Hub Genes and immune cell infiltration abundances within the Combined Dataset. ns on behalf of the p value ≥ 0.05, no statistical significance; *, p value < 0.05, statistically significant; **, p value < 0.01, highly statistically significant; ***, p value < 0.001 and highly statistically significant. In the group comparison plots, orange indicates Control samples, while grey indicates DN samples. The absolute values of correlation coefficients (r values) indicate relationship strength: values below 0.3 suggest weak or negligible correlation, 0.3–0.5 suggest weak correlation, 0.5–0.8 suggest moderate correlation, and values above 0.8 suggest strong correlation. In the correlation heatmap, red denotes positive correlation, while blue denotes negative correlation. The intensity of colors reflects the magnitude of correlation strength. ssGSEA single-sample Gene-Set Enrichment Analysis, DN Diabetic nephropathy