The involvement of M2-like tumor-associated macrophages (TAMs) in the advancement and treatment of cancer has been widely documented. This study aimed to develop a new signature associated with M2-like TAMs to predict the prognosis and treatment response in individuals diagnosed with breast cancer (BC). Weighted gene co-expression network analysis (WGCNA) was used to identity for M2-like TAM-related modular genes. The M2-like TAM-related modular subtype was identified using unsupervised clustering. WGCNA identified 722 M2-like TAM genes, 204 of which were associated with recurrence-free survival (RFS). Patients in cluster 1 exhibited upregulated cancer-related pathways, a higher proportion of triple-negative breast cancer (TNBC) subtypes, lower expression of immune checkpoints, and worse prognosis. Cluster 2 was characterized by upregulated immune-related pathways, a higher proportion of luminal A subtypes, and higher expression of immune checkpoints. A prognostic signature was created and confirmed using an independent dataset. A well-built nomogram can accurately forecast the survival outcomes for every individual. Furthermore, patients classified as low-risk exhibited a more favorable outlook, elevated tumor microenvironment (TME) score, and superior reaction to immunotherapy. In conclusion, we discovered 2 different types of M2-like TAMs and developed a prognostic signature revealing the diversity of M2-like TAMs in BC and their correlation with immune status and prognosis. This feature can predict the prognosis and immunotherapeutic effects of BC and offer novel concepts and approaches for tailoring BC treatment.
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