Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity -related biological features can effectively screen high -risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity -gene clinical prognostic index (LOG -CPI), combining a 7 -gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5 -year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOGCPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.