Background: Breast cancer is nowthe most prevalent malignant among female population worldwide. Anoikis is a key progress during genesis and metastasis of malignant cells. Pyroptosis is a newly defined type of programmed cell death reported to have a dual effect on the development of carcinomas and had been reported to have the potential to affect anti-tumor immunity. However, few studies investigated the connections between anoikis, pyroptosis and prognosis in breast cancer. Methods: Anoikis and pyroptosis-related genes (APGs) were achieved from GeneCards and Harmonizome portals database. Based on expression profiles of APGs of patients from TCGA-BRCA cohort, differentiated expressed APGs between normal and tumoral tissues are identified. Next, by univariate Cox regression analysis of combined data of TCGA and GSE cohorts, prognostic APGs was defined. Then patients from both TCGA and GEO cohort were classified into three clusters by consensus clustering algorithm. Overlapped APGs between three clusters were identified as intersecting genes, based on expression of which, individuals are again assigned to two different gene clusters. Eventually, we successfully developed a PCA scoring signature and a nomogram system to accurately predict the prognosis and immunotherapy efficacy of breast cancer patients. Results: Patients were classified into three clusters based on APGs’ expression. Cluster A was featured by longest OS. According to the expression profile of 300 intersecting genes, patients were again divided into two different gene clusters. Subtype B is characterized with poorer diagnosis. Meanwhile, by means of principal component analysis, we successfully predicted clinical outcomes and treatment response to immunotherapy. Finally, we constructed an APG score-associated nomogram model to predict prognosis. Conclusion: We successfully established a scoring system based on anoikis and pyroptosis-related genes, as well as combined with clinicopathological features, to serve as a biomarker for prediction of clinical outcomes and immunotherapy efficacy in breast cancer.

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.

Background The metabolic reprogramming of breast cancer (BC) has gained great attention in recent years. Malignant and infiltrating immune cells compete for nutrients and metabolites; still, the impact of metabolism on them remains to be further elucidated. The specific objective of this analysis was to anatomy the action of immune-related metabolic genes in breast cancer and develop a combined model to predict susceptibility to immunotherapy, thus helping guide patient management and establish personalized risk assessment with superior accuracy and clinical applicability.Methods This study was based on data of 1048 BC patients from The Cancer Genome Atlas (TCGA). 46 immune-related metabolic genes were identified by differential expression analysis between different tissue states. Applying unsupervised clustering and other bioinformatics techniques, we illustrated how the divergent groups' immunometabolism and survival conditions varied. A comprehensive risk-sharing index model was developed using LASSO regression and multivariable Cox analysis method, and BC patients were categorized into two risk groups based on their levels of risk score. Another three independent GEO database sets [GSE20685, GSE42568, GSE124647] were selected for external validation. Finally, the single-cell sequencing data mining and analysis aimed to explore the immunometabolic heterogeneity of human breast cancers.Results Fourteen immune-related metabolic signatures (FABP6, LPA, RBP4, CETP, STAB2, PPARG, TYMP, CGA, GCGR, SDC1, BGN, ABCA1, PLA2G4A, PLK1) were identified for use in constructing a comprehensive prognostic model for BC. The high-risk group was characterized by poorer diagnosis, fewer activated immune cell infiltration and better treatment response to immune checkpoint inhibitors. Moreover, the index was combined with clinical parameters, weighted, and created a nomogram. It is imperative to point out that our model and corresponding nomogram are optimal and independent prognosis factors compared to other traditional clinical variables. They also have satisfactory predictive capacity validated by ROC curve, calibration plot and DCA analysis.Conclusions Our 14-MRDEGs and their multiple integrations reflected genetic-level and immunometabolic profile alterations in BC, allowing accurate prediction of survival risk and the efficacy of immunotherapy. The research conclusions may provide a reference for further analysis and drug development in target discovery.