■氧化应激和细胞衰老(OSCS)对三阴性乳腺癌(TNBC)的发生和进展有很大影响。本研究旨在构建基于氧化应激和细胞衰老相关差异表达基因(OSCSRDEGs)的TNBC预后模型。
■癌症基因组图谱(TCGA)数据库和两个基因表达综合(GEO)数据库用于鉴定OSCSRDEGs。使用单样本基因集富集分析(ssGSEA)检查OSCSRDEGs与免疫浸润之间的关系,估计,和CIBERSORT算法。最小绝对收缩和选择算子(LASSO)回归分析,采用Cox回归和Kaplan-Meier分析构建预后模型。接收器工作特性(ROC)曲线,列线图,采用决策曲线分析(DCA)评价预后疗效。基因集富集分析(GSEA)基因本体论(GO),和京都基因和基因组百科全书(KEGG)被用来探索潜在的功能和机制。
■综合分析确定了总共27个OSCSRDEG,其中选择了15个基因来开发预后模型。观察到来自该模型的风险评分具有高度的统计学显著性,以准确地预测TNBC总体生存率。决策曲线分析(DCA)和ROC曲线分析进一步证实了OSCSRDEGs预后模型在预测疗效方面的优越准确性。值得注意的是,列线图分析显示,DMD在模型中的效用最高.在高和低OSCScore组之间的比较,在TCGA-TNBC数据集中,免疫细胞的浸润丰度存在统计学差异.
■这些研究有效地确定了四个基本的OSCSRDEG(CFI,DMD,NDRG2和NRP1),并为诊断为TNBC的个体精心开发了与OSCS相关的预后模型。这些发现有可能大大有助于理解OSCS在TNBC中的参与。
UNASSIGNED: Oxidative stress and cellular senescence (OSCS) have great impacts on the occurrence and progression of triple-negative breast cancer (TNBC). This study was intended to construct a prognostic model based on oxidative stress and cellular senescence related difference expression genes (OSCSRDEGs) for TNBC.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: These studies have effectively identified four essential OSCSRDEGs (CFI, DMD, NDRG2, and NRP1) and meticulously developed an OSCS-associated prognostic model for individuals diagnosed with TNBC. These discoveries have the potential to significantly contribute to the comprehension of the involvement of OSCS in TNBC.