OBJECTIVE: Breast cancer was the most common type of cancer among women worldwide, significantly impacting their quality of life and survival rates. And obesity has been widely accepted as an important risk factor for breast cancer. However, the specific mechanisms by which obesity affects breast cancer were still unclear. Therefore, studying the impact mechanisms of obesity as a risk factor for breast cancer was of utmost importance. METHODS: This study was based on TCGA breast cancer RNA transcriptomic data and the GeneCard obesity gene set. Through single and multiple factor Cox analysis and LASSO coefficient screening, seven hub genes were identified. The independent mechanisms of these seven hub genes were evaluated from various aspects, including survival data, genetic mutation data, single-cell sequencing data, and immune cell data. Additionally, the risk prognosis model and the neural network diagnostic model were established to further investigate these seven hub genes. In order to achieve precision treatment for breast cancer (BRCA), based on the RNA transcriptomic data of the seven genes, 1226 BRCA patients were divided into two subtypes: BRCA subtype 1 and BRCA subtype 2. By studying and comparing the immune microenvironment, investigating the mechanisms of differential gene expression, and exploring the mechanisms of subnetworks, we aim to explore the clinical differences in the presentation of BRCA subtypes and achieve precision treatment for BRCA. Finally, qRT-PCR experiments were conducted to validate the conclusions of the bioinformatics analysis. RESULTS: The 7 hub genes showed good diagnostic independence and can serve as excellent biomarkers for molecular diagnosis. However, they do not perform well as independent prognostic molecular markers for BRCA patients. When predicting the survival of BRCA patients, their AUC values at 1 year, 3 years, and 5 years are mostly below 0.5. Nevertheless, through the establishment of the risk prognosis model considering the combined effect of the seven hub genes, it was found that the survival prediction of BRCA patients can be significantly improved. The risk prognosis model, compared to the independent use of the seven hub genes as prognostic markers, achieved higher timeROC AUC values at 1 year, 3 years, and 5 years, with values of 0.651, 0.669, and 0.641 respectively. Additionally, the neural network diagnostic model constructed from the 7 genes performs well in diagnosing BRCA, with an AUC value of 0.94, accurately identifying BRCA patients. The two subtypes identified by the seven hub genes exhibited significant differences in survival period, with subtype 1 having a poor prognosis. The differential mechanisms between the two subtypes mainly originate from regulatory differences in the immune microenvironment. Finally, the results of this study's bioinformatics analysis were validated through qRT-PCR experiments. CONCLUSION: 7 hub genes serve as excellent independent biomarkers for molecular diagnosis, and the neural network diagnostic model can accurately distinguish BRCA patients. In addition, based on the expression levels of these seven genes in BRCA patients, two subtypes can be reliably identified: BRCA subtype 1 and BRCA subtype 2, and these two subtypes showed significant differences in BRCA patient survival prognosis, proportion of immune cells, and expression levels of immune cells. Among them, patients with subtype 1 of BRCA had a poor prognosis.