Identification of high-risk signatures and therapeutic targets through molecular characterization and immune profiling of TP53-mutant breast cancer

Abstract

Background

TP53 mutations are commonly observed in aggressive subtypes of breast cancer, influencing the tumor microenvironment (TME) and patient prognosis. In this study, we developed a prognostic gene-based risk model to stratify TP53-mutant breast cancer patients and explore potential therapeutic targets.

Methods

We performed comprehensive bioinformatics analyses using TCGA and METABRIC datasets to identify key prognostic genes in TP53-mutant breast cancer. Differential expression and Gene Set Enrichment Analysis (GSEA) revealed dysregulated pathways, while protein–protein interaction (PPI) networks highlighted functional hubs. Survival analysis, followed by univariate Cox regression, LASSO, and multivariate regression, led to the construction of a robust gene-based risk model. Immune landscape profiling was conducted to evaluate tumor microenvironment characteristics. Finally, drug sensitivity analysis and molecular docking were used to identify potential therapeutic agents targeting high-risk patients.

Results

TP53 mutations were present in ∼ 35 % of patients and associated with significant transcriptomic alterations. A total of 666 genes were consistently dysregulated, including 333 upregulated (such as A2ML1, CA9, VGLL1, PSAT1) and 333 downregulated (such as AGR3, TFF1, ESR1, CPB1) in TP53 mutated breast cancer patients. GSEA revealed that the cell cycle, DNA replication, and metabolic pathways in in TP53 mutated breast cancer patients. Protein–protein interaction (PPI) network analysis of these genes revealed tightly connected modules related to mitotic regulation and immune signaling, underscoring key functional hubs in TP53-mutant tumors. A four-gene prognostic model (FGFR4, S100P, ADM, CTSC) stratified TP53-mutant patients into high- and low-risk groups with distinct survival outcomes and immune profiles. High-risk patients exhibited a suppressed immune landscape, characterized by lower immune and stromal cell infiltration and higher tumor purity. Drug sensitivity analysis and molecular docking revealed several compounds, including Lapatinib, Docetaxel, and Trametinib, with strong binding affinities to key model genes. These drugs demonstrated potential efficacy in high-expression cells, suggesting their viability as targeted therapies.

Conclusion

Our findings underscore the prognostic value of the identified genes and the immunosuppressive TME in TP53-mutant breast cancer. The identification of drug candidates with strong binding affinities to key proteins provides promising avenues for targeted therapy in this high-risk patient population.