Identification and validation of a novel anoikis-related signature to predict clinical outcomes, TME and treatment response of breast cancer patients

Abstract

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.