基于肿瘤干细胞免疫相关基因的乳腺癌预后风险评分模型OA

Objective Based on TCGA-BRCA data,we integrated cancer stem cell(CSC)related genes with immune-related gene information to construct and evaluate a prognostic risk model for breast cancer patients.Methods Gene expression profiles and clinical data from 1,073 breast cancer patients in The Cancer Genome Atlas(TCGA-BRCA)were collected.Weighted gene co-expression network analy-sis(WGCNA)was applied to detect co-expression modules of immune-related genes.CSC-associated genes were retrieved from the BCSCdb database and intersected with immune-related genes and differen-tially expressed genes(DEGs)to obtain 70 candidate genes.Least absolute shrinkage and selection operator(LASSO)Cox regression followed by multivariable Cox regression were used to further select key genes and build a 12-gene prognostic risk model.Model performance was assessed by Kaplan-Meier survival analysis,time-dependent receiver operating characteristic(ROC)curves,and nomogram analysis.Gene set enrichment analysis(GSEA)and immune infiltration analyses were performed to explore under-lying biological mechanisms.Results The CSC-related immune prognostic model stratified patients into high-and low-risk groups with significantly different overall survival.Time-dependent ROC analysis indi-cated good predictive ability for 1-,3-,and 5-year survival(area under the curve[AUC]=0.81,0.77,and 0.75,respectively).The nomogram demonstrated favorable discriminative performance with a C-index ranging from 0.78 to 0.81.GSEA and immune infiltration analyses provided further insight into pathways and immune features associated with risk stratification.Conclusion We developed a prognostic risk model that integrates CSC-related immune gene expression information in breast cancer.The model effec-tively discriminated between high-and low-risk patients and shows promising prognostic value.