ArticleFrontiers in genetics2025
Identification of cellular senescence-related genes as biomarkers for lupus nephritis based on bioinformatics.
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Mitochondrial dysfunction in myeloid cells: a central deficit in autoimmune diseases.Trends in immunology · 2025Review
- DNA Methylation Profiling in Childhood-Onset Lupus Reveals Distinct Epigenetic Clusters and Suggests Epigenetic Drivers of Disease Activity.medRxiv : the preprint server for health sciences · 2025Article
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5 authors.
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Abstract
Background: Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus with unclear pathogenesis. The most accurate diagnosis criterion of LN is still renal biopsy and nowadays treatment strategies of LN are far from satisfactory. Cellular senescence is defined as the permanent cell cycle arrest marked by senescence-associated secretory phenotype (SASP), which has been proved to accelerate the mobility and mortality of patients with LN. The study is aimed to identify cellular senescence-related genes for LN. Methods: Genes related to cellular senescence and LN were obtained from the MSigDB genetic database and GEO database respectively. Through differential gene analysis, Weighted Gene Go-expression Network Analysis (WGCNA) and machine learning algorithms, hub cellular senescence-related differentially expressed genes (CS-DEGs) were identified. By external validation, hub CS-DEGs were further filtered and the remaining genes were identified as biomarkers. We explored their potential physiopathologic function through GSEA. Results: We obtained 432 genes related to cellular senescence, 1,208 differentially expressed genes (DEGs) and 840 genes in the key gene module related to LN, which were intersected with each other for CS-DEGs. Subsequent Machine learning algorithms screened out six hub CS-DEGs and finally three hub CS-DEGs, ALOX5, PTGER2 and PRKCB passed through external validation, which were identified as biomarkers. The three biomarkers were enriched in "B Cell receptor signaling pathway" and "NF-kappa B signaling pathway" based on GESA results. Conclusion: This study explored the potential relationship between cellular senescence and LN, and identified three biomarkers ALOX5, PTGER2, and PRKCB playing key roles in LN, which will provide new insights for the diagnosis and treatment of LN.
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