ArticleDiscover oncology2025
Cellular senescence and disulfide death-related genes as biological markers of breast cancer prognosis.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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4 authors.
Funding
Abstract
objectiveBreast cancer is a heterogeneous disease with diverse prognosis and treatment outcomes. The aim of this study was to reveal the genes related to cellular senescence and disulfide death in breast cancer and to explore their potential mechanisms and clinical applications in breast cancer.
methodsIn this study, we screened differential genes associated with cellular senescence and disulfide death based on publicly available data, constructed a protein-protein interaction network (PPI Network), and explored the functions of differential genes by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A prognostic risk model was constructed in the breast cancer dataset TCGA-BRCA, and a single multifactorial Cox regression was performed to assess the effect of differential genes on prognosis based on clinical information. Gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) were performed based on the median values of key prognostic gene risk scores grouped together.
resultIn this study, 17 differential genes associated with cellular senescence and disulfide death were screened. Single multifactor Cox regression analysis was performed to construct a prognostic risk model for breast cancer, and the results showed that the LASSO regression model contained 2 LASSO regression model genes: ACTN2, CHD4. Combined with the clinical information, the utility of the LASSO risk score and pathological stage for the prognostic risk model for breast cancer was significantly higher than that of the other variables; in addition, our constructed multifactor Cox regression model had a clinical predictive effect of 5 years > 3 years > 1 year.
conclusionPredictive models constructed based on genes related to cellular senescence and disulfide death predict the prognosis of breast cancer patients.
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