乳腺癌（BC）是全球女性中最常见的恶性肿瘤，也是女性癌症相关死亡的重要原因。最近的研究表明，脂质代谢相关基因 (LMRG) 在包括 BC 在内的各种类型的肿瘤中表现出预后潜力。我们的研究旨在建立一种预测 BC 转移的新模型。从癌症基因组图谱和基因表达综合数据库中下载 BC 患者的临床信息和相应的 RNA 数据。进行共识聚类来识别新的分子亚组。使用表达、微环境细胞群计数器、微环境细胞群计数器和单样本基因集富集分析来估计恶性肿瘤组织中的基质细胞和免疫细胞，以确定肿瘤免疫微环境和已识别亚群的免疫状态。进行功能分析，包括基因本体论和基因集富集分析，以阐明潜在的机制。使用最小绝对收缩和选择算子算法以及多变量 Cox 回归分析构建了预后风险模型。本研究使用公共数据库确定了出现转移的 BC 患者和未发生转移的患者之间的差异基因表达。利用获得的数据，我们建立了基于六个 LMRG 的预测模型。此外，共识聚类和预后评分分组分析表明，差异表达的LMRGs通过调节肿瘤免疫影响肿瘤预后。为了促进临床应用，我们开发了一个整合风险模型和临床特征的列线图，以准确预测 BC 患者的预后。我们开发并验证了与 LMRG 相关的新特征，用于预测 BC 患者的无病生存期。 LMRGs 的表达与 BC 患者的免疫微环境相关，为 BC 的诊断和治疗提供新的见解和改进的策略。© 2024。作者。

Breast cancer (BC) is the most prevalent malignant tumor among women worldwide and a significant cause of cancer-related deaths in females. Recent studies have shown that lipid metabolism-related genes (LMRGs) exhibit prognostic potential in various types of tumors, including BC. Our study aimed to establish a novel model to predict the metastasis of BC.Clinical information and corresponding RNA data of patients with BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering was performed to identify novel molecular subgroups. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression, microenvironment cell populations counter, microenvironment cell populations counter, and single-sample gene set enrichment analyses were employed to determine the tumor immune microenvironment and immune status of the identified subgroups. Functional analyses, including Gene Ontology and gene set enrichment analyses, were conducted to elucidate the underlying mechanisms. A prognostic risk model was constructed using the Least Absolute Shrinkage and Selection Operator algorithm and multivariate Cox regression analysis.This study identified differential gene expression between patients with BC exhibiting metastasis and those without metastasis using public databases. Using the obtained data, we established predictive models based on six LMRGs. Furthermore, consensus clustering and prognostic score grouping analysis revealed that differentially expressed LMRGs influence tumor prognosis by regulating tumor immunity. To facilitate clinical application, we developed a nomogram integrating the risk model and clinical characteristics to accurately predict the prognosis of patients with BC.We developed and validated a novel signature associated with LMRGs for predicting disease-free survival in patients with BC. The expression of LMRGs correlates with the immune microenvironment of patients with BC, providing new insights and improved strategies for the diagnosis and treatment of BC.© 2024. The Author(s).