ArticleHeliyon2025
Identification and functional analysis of energy metabolism and pyroptosis-related genes in diabetic nephropathy.
Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Energy metabolism and pyroptosis are integral to the pathogenesis of diabetic nephropathy (DN). However, the precise roles of energy metabolism and pyroptosis in DN development remain unclear. This study aims to elucidate the roles of energy metabolism- and pyroptosis-related differentially expressed genes (EMAPRDEGs) in DN development. Methods: EMAPRDEGs were identified by querying the GeneCards and Gene Expression Omnibus (GEO) databases. Subsequent analyses included Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) network analysis. Additionally, mRNA-miRNA, mRNA-drug, and mRNA-transcription factor (TF) interaction networks were constructed. Differential expression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic potential of EMAPRDEGs. Immune cell infiltration in DN was assessed using the ssGSEA algorithm, and the expression levels of EMAPRDEGs in DN tissues were validated by quantitative real-time PCR (qRT-PCR). Results: Thirteen EMAPRDEGs were identified, with GO and KEGG analyses indicating their involvement in energy metabolism pathways. GSEA revealed significant enrichment of these genes in biological pathways associated with diabetic nephropathy. PPI network analysis highlighted the central role of these genes within the relevant pathways. Predictive modeling demonstrated interactions between EMAPRDEGs, 69 miRNAs, and 117 TFs. Immune infiltration analysis showed substantial alterations in immune cell populations, with Conclusion: Bioinformatics analysis identified 13 candidate EMAPRDEGs, among which
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