基于失巢凋亡相关基因的乳腺癌预后模型构建与验证OA北大核心CSTPCD

Objective To construct a prognostic model of anoikis-related genes(ARGs)in breast cancer(BC)and provide more effective guidance for clinical practice.Methods The transcriptome and clinical data of patients with BC were collected from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases.The ARGs were divided into types A and B,and survival and pathway analyses were performed.Cox regression and least absolute shrinkage and selection operator(LASSO)regression were used to analyze variations in the survival prognosis,immune microenvironment,and tumor microenvironment.The prognostic ARGs were used to construct a prognostic model.Finally,the expression of prognosis-related ARGs in BC cells was verified using real-time polymerase chain reaction(PCR).Results Ten ARGs related to poor prognosis of BC were identified,and the risk scores of these ten ARGs were used as inde-pendent prognostic factors for patients with BC.Finally,the risk score was combined with the clinicopathological features of BC to con-struct a nomogram.The results of the decision curve analysis showed that the patients in this model would benefit from clinical treatment strategies.Real-time PCR analysis showed that the expression levels of YAP1,PIK3R1,BAK1,PHLDA2,EDA2R,CD24,SLC2A1,and CDC25Cwere upregulated in BC cells,whereas SLC39A6and LAMB3in BC cells were downregulated.Conclusion ARGs can be used as biomarkers for risk stratification and survival prediction in patients with BC and provide a basis for individualized and precise treatment of these patients.