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TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.

Background Breast cancer is the most prevalent malignant among female population worldwide. Anoikis is a key progress during genesis and metastasis of malignant cells. Few studies investigate connections between anoikis and prognosis in breast cancer patients. Methods Anoikis-related genes (ARGs) were achieved from GeneCards and Harmonizome portals database. Based on expression patterns of prognostic ARGs, patients were classified as two subtypes and an ARG risk signature was constructed. Based on the formulation, risk score of every individual was calculated. Then, the ability of prognosis prediction was examined by ROC curve and Nomogram. Finally, we analyzed the correlation between TME, signal pathways enriched and treatment response between different risk groups. Results Patients were classified into two clusters based on ARG expression. Cluster B was featured by a longer OS. According to the expression profile of prognostic ARGs between clusters, we constructed a risk scoring signature based on five genes. Patients were again divided into the high- and low-risk group according to the score. The high-risk group was characterized by poorer diagnosis, fewer activated immune cells infiltration and worse treatment response to immune checkpoint inhibitors. Finally, the drug sensitivity analysis revealed the potential benefit of the model in supporting clinical decision. Conclusion We successfully established an ARG risk scoring system associating expression profile of ARGs with clinicopathological features to make breast cancer management more individualized and rationalized.