ArticleBMC nephrology2023
Identifying cellular senescence associated genes involved in the progression of end-stage renal disease as new biomarkers.
Article in BMC nephrology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Gut dysbiosis and constipation in patients with end-stage renal disease: pathogenic associations and therapeutic interventions.Frontiers in cellular and infection microbiology · 2026Review
- Identification and validation of parthanatos-related genes in end-stage renal disease.Renal failure · 2025Article
- Development and validation of a comprehensive machine learning framework for a diagnostic model of uremia based on genes involved in major depressive disorder.Frontiers in nephrology · 2025Article
- Renal fibroblasts are involved in fibrogenic changes in kidney fibrosis associated with dysfunctional telomeres.Experimental & molecular medicine · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCellular senescence plays an essential role in the development and progression of end-stage renal disease (ESRD). However, the detailed mechanisms phenomenon remains unclear.
methodsThe mRNA expression profiling dataset GSE37171 was taken from the Gene Expression Omnibus (GEO) database. The cell senescence-associated hub genes were selected by applying protein-protein interaction (PPI), followed by correlation analysis, gene interaction analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. We next explored the relationships of hub genes with miRNAs, TFs, and diseases. The absolute abundance of eight immune cells and two stromal cells were calculated by MCPcount and the correlation of hub genes with these ten cells was analyzed. Lasso was used to selecting for trait genes. ROC curves and DCA decision curves were used to assess the accuracy and predictive power of the trait genes.
resultsA total of 65 cellular senescence signature genes were identified among patients and controls. The PPI network screened out ten hub genes. GO and KEGG indicated that ten hub genes were associated with ESRD progression. Transcription factor gene interactions and common regulatory networks of miRNAs were also identified in the datasets. The hub genes were significantly correlated with immune cells and stromal cells. Then the lasso model was constructed to screen out the five most relevant signature genes (FOS, FOXO3, SIRT1, TP53, SMARCA4). The area under the ROC curve (AUC) showed that these five characteristic genes have good resolving power for the diagnostic model.
conclusionsOur findings suggested that cellular senescence-associated genes played an important role in the development of ESRD and immune regulation.
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