Identification and Characterization of Cytokine Genes in Breast Cancer for Predicting Clinical Outcomes

Breast cancer is a highly heterogeneous disease with diverse clinical outcomes and treatment responses. While traditional molecular subtypes have improved patient stratification, they fail to fully capture the immune heterogeneity that influences tumor progression and therapy efficacy. Cytokines play a central role in regulating immune responses within the tumor microenvironment, yet their systematic profiling in breast cancer remains unexplored. Here we conducted a comprehensive analysis of cytokine expression across breast cancer patients using transcriptomic and clinical data. Prognostic cytokines were identified via survival analysis, and a cytokine-based molecular classification was established through consensus clustering. We identified three cytokine-driven breast cancer subtypes with distinct transcriptional profiles, immune infiltration patterns, and clinical outcomes. A cytokine-derived risk score was then developed using lasso regression and validated in external datasets to assess its predictive power for patient survival and treatment response. We then characterized the immune microenvironment of patients with different risk scores using immune infiltration analysis and single-cell RNA sequencing (RNA-seq) data. The risk score effectively stratified patients into high- and low-risk groups, with significant differences in survival outcomes. The low-risk subtype exhibited enhanced immune cell infiltration and stronger immune cell interactions, while the high-risk subtype was associated with an immunosuppressive microenvironment and a worse prognosis. Notably, patients with low-risk scores demonstrated superior responses to both immunotherapy and chemotherapy, highlighting the clinical relevance of cytokine-based classification. Our study provides the first comprehensive cytokine-based molecular subtyping of breast cancer, revealing distinct immune landscapes and prognostic implications. The cytokine-derived risk score offers a powerful tool for predicting patient survival and treatment response, with potential applications in personalized medicine and immunotherapy strategies. These findings underscore the critical role of cytokines in shaping breast cancer heterogeneity and highlight their value as biomarkers for therapeutic decision-making.