Integrative analyses of genes associated with oxidative stress and cellular senescence in triple-negative breast cancer

Abstract

Abstract Purpose This study was intended to construct a prognostic model based on oxidative stress and cellular senescence related difference expression genes (OSCSRDEGs) for TNBC. Methods The Cancer Genome Atlas (TCGA) databases and two Gene Expression Omnibus (GEO) databases were used to identify OSCSRDEGs. The relationship between OSCSRDEGs and immune infiltration was examined using single-sample gene-set enrichment analysis (ssGSEA), ESTIMATE, and the CIBERSORT algorithm. Least absolute shrinkage and selection operator (LASSO) regression analyses, Cox regression and Kaplan-Meier analysis were employed to construct a prognostic model. Receiver operating characteristic (ROC) curves, nomograms, and decision curve analysis (DCA) were used to evaluate the prognostic efficacy. Gene Set Enrichment Analysis (GSEA) Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the potential functions and mechanism. Results A comprehensive analysis identified a total of 27 OSCSRDEGs, out of which 15 genes selected for development of a prognostic model. A high degree of statistical significance was observed for the riskscores derived from this model to accurately predict TNBC Overall survival. The decision curve analysis (DCA) and ROC curve analysis further confirmed the superior accuracy of the OSCSRDEGs prognostic model in predicting efficacy. Notably, the nomogram analysis highlighted that DMD exhibited the highest utility within the model. In comparison between high and low OSCScore groups, the infiltration abundance of immune cells was statistically different in the TCGA-TNBC dataset. Conclusion Our study identified four essential OSCSRDEGs (CFI, DMD, NDRG2, and NRP1) and precisely constructed an OSCS-related prognostic model for patients with TNBC.