Construction of a prognostic model and analysis of related mechanisms in breast cancer based on multiple datasets

Background: Breast cancer (BC) is a common tumor among women and is a heterogeneous disease with many subtypes. Each subtype shows different clinical presentations, disease trajectories and prognoses, and different responses to neoadjuvant therapy; thus, a new and universal prognostic biomarker for BC patients is urgently needed. Our goal is to identify a novel prognostic molecular biomarker that can accurately predict the outcome of all BC subtypes and guide their clinical management. Methods: Utilizing data from The Cancer Genome Atlas (TCGA), we analyzed differential gene expression and patient clinical data. Weighted gene coexpression network analysis (WGCNA), Cox univariate regression and least absolute shrinkage and selection operator (LASSO) analysis were used to construct a prognostic model; the differential expression of the core genes in this model was validated via real-time quantitative polymerase chain reaction (RT-qPCR), and the reliability of the predictive model was validated in both an internal cohort and a BC patient dataset from the Gene Expression Omnibus (GEO) database. Further studies, such as gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA), were performed to investigate the enrichment of signaling pathways. The CIBERSORT algorithm was used to estimate immune infiltration and tumor mutation burden (TMB), and drug sensitivity analysis was performed to evaluate the treatment response. Results: A total of 1,643 differentially expressed genes were identified. After WGCNA and Cox regression combined with LASSO analysis, 15 genes were identified by screening and used to establish a prognostic gene signature. Further analysis revealed that the epithelial-mesenchymal transition (EMT) pathway gene signature was enriched in these genes. Each patient was assigned a risk score, and according to the median risk score, patients were classified into a high-risk group or a low-risk group. The prognosis of the low- risk group was better than that of the high-risk group (P<0.01), and analyses of two independent GEO validation cohorts yielded similar results. Furthermore, a nomogram was constructed and found to perform well in predicting prognosis. GSVA revealed that the EMT pathway, transforming growth factor beta (TGF-beta) signaling pathway and PI3K-Akt signaling pathway genes were enriched in the high-risk group, and the Wnt-beta-catenin signaling pathway, DNA repair pathway and P53 pathway gene sets were enriched in the low-risk group. GSEA revealed genes related to TGF-beta signaling and the PI3K-Akt signaling pathways were enriched in the high-risk group. CIBERSORT demonstrated that the low-risk group had greater infiltration of antitumor immune cells. The TMB and drug sensitivity results suggested that immunotherapy and chemotherapy are likely to be more effective in the low-risk group. Conclusions: We established a new EMT pathway-related prognostic gene signature that can be used to effectively predict BC prognosis and treatment response.