<b>Leveraging Machine Learning for Profiling Lipidomic Alterations in Breast Cancer Tissues: A Methodological Perspective</b>

The lipidomic datasets utilized in this study comprise LC-MS (Liquid Chromatography-Mass Spectrometry) data in positive and negative modes, derived from breast tumor samples obtained through the METACancer FP7 project. The sample collection process and ethical approvals have been previously documented by Hilvo et al. and Denkert et al. The breast cancer samples were categorized based on the expression status of specific biomarkers: HER2, ER (Estrogen Receptor), and PR (Progesterone Receptor) (Metadata).本研究使用的脂组学数据集包含液相色谱-质谱联用法（Liquid Chromatography-Mass Spectrometry，LC-MS）正负离子模式下的数据，其样本来源为METACancer FP7项目获取的乳腺肿瘤样本。样本采集流程与伦理审批事宜已由Hilvo等人及Denkert等人在既往研究中完成记录。本研究中的乳腺癌样本依据特定生物标志物的表达状态进行分类：HER2、雌激素受体（Estrogen Receptor，ER）以及孕激素受体（Progesterone Receptor，PR）（元数据Metadata）。