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

Hong Chen; Xiaoxia Su; Yan Li; Cui Dang; Zuojie Luo

doi:10.1186/s13098-024-01531-5

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

Diabetol Metab Syndr. 2024 Nov 28;16(1):287. doi: 10.1186/s13098-024-01531-5.

Authors

Hong Chen^#^{1

2}, Xiaoxia Su^#², Yan Li¹, Cui Dang¹, Zuojie Luo³

Affiliations

¹ Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
² Department of Nephrology, The Second People's Hospital of Qinzhou, Guangxi, China.
³ Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. zluo888@163.com.

^# Contributed equally.

PMID: 39609849
DOI: 10.1186/s13098-024-01531-5

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.