Abstract PS4-02-13: Genes from epithelial-mesenchymal transition predict overall survival effectively in breast cancer: anovel risk model based on initial step of tumor metastasis

Abstract Background: Accumulating evidence has demonstrated that epithelial-mesenchymal transition (EMT) plays a critical role in breast cancer (BRCA) initiation, invasion, metastasis, and prognosis. BRCA still has a high mortality rate owing to recurrence and metastasis. As a core component of the biological signal regulatory network, EMT urgently requires accurate biomarkers to evaluate the prognosis of BRCA. Methods: We obtained 1223 BRCA samples from The Cancer Genome Atlas and 1184 EMT-related genes from the dbEMT2.0 public database. Differentially expressed genes were filtered, and univariate Cox and LASSO-Cox regression analyses were performed to select prognosis-related gene signatures. A novel risk score model was developed and validated using external cohorts. Moreover, mutual confirmation between KEGG/GO and gene set enrichment analyses was performed to reveal potential molecular mechanisms. Finally, a nomogram including the risk score model and several clinical parameters was established to help individualize patients’ survival predictions. Results: A total of 381 differentially expressed EMT-associated genes (EAGs) were identified and 15 EAGs relevant to survival prognosis were used to calculate the risk score formula. Patients were divided into high- and low-risk groups based on the specific risk score model. Samples in the low-risk group had a higher overall survival. One UCSC cohort and three Gene Expression Omnibus cohorts verified the reliability of the model. A nomogram was established to predict survival, including the risk score, age, pathological N stage, and pathological M stage. Ultimately, the results of gene set enrichment analysis revealed that the potential molecular mechanism was mainly concentrated in the extracellular region. Conclusions: The 15-gene signature model based on EMT is significant for predicting overall survival and revealing possible molecular mechanisms as potential targets and tools for pharmacologic and regenerative medicine biomarkers. Citation Format: W. Chen, X. Qin, X. Xu, X. Lang, G. Xie, W. Liang. Genes from epithelial-mesenchymal transition predict overall survival effectively in breast cancer: anovel risk model based on initial step of tumor metastasis abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-02-13.