Constructing a novel prognostic signature of tumor driver genes for breast cancer

Objectives: To systematically explore the function and prognostic ability of tumor-driver genes (TDGs) in breast carcinoma (BRCA). Methods: Functional enrichment analysis of BRCA differentially expressed TDGs was assesed. We used univariate Cox, lasso, and multivariate Cox regression to identify the independent prognostic TDGs of BRCA. Then we constructed a prognostic signature and verified its predictive performance. Gene set enrichment analysis of the signal pathway revealed the differences between the prognostic signature high- and low-risk groups. Finally, a nomogram related to the prognostic model was established and verified. Results: A total of 595 differentially expressed TDGs were identified, which are related to various molecular mechanisms of BRCA progression. We identified 8 independent prognostic TDGs for BRCA and validated their expression and prognosis with public data and clinical samples. The BRCA cohort was divided into training and validation cohorts, and prognostic signatures were constructed separately. The log-rank test showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group in the prognostic signature (P<0.001); the AUC in the three cohorts were 0.805, 0.712, and 0.760, respectively; the nomogram also showed better predictive performance. Analyzing the difference between the two risk subtypes, the high-risk group is mainly enriched in angiogenesis, MTORC1, epithelial-mesenchymal transition and glycolysis, which means it is highly malignant. Conclusions: The prognostic signature and nomogram was confirmed to accurately predict the prognosis of patients with BRCA and we validated the hub genes, suggesting their potential as future therapeutic targets.