<b>Uncovering Subtype-Specific Metabolic Signatures in Breast Cancer through Multimodal Integration, Attention-Based Deep Learning, and Self-Organizing Maps</b>

This study integrates multimodal metabolomic data from three platforms—LC-MS, GC-MS, and NMR—to systematically identify biomarkers distinguishing breast cancer subtypes. A feedforward attention-based deep learning model effectively selected 99 significant metabolites, outperforming traditional static methods in classification performance and biomarker consistency. By combining data from diverse platforms, the approach captured a comprehensive metabolic profile while maintaining biological relevance. Self-organizing map analysis revealed distinct metabolic signatures for each subtype, highlighting critical pathways. Group 1 (ER/PR-positive, HER2-negative) exhibited elevated serine, tyrosine, and 2-aminoadipic acid levels, indicating enhanced amino acid metabolism supporting nucleotide synthesis and redox balance. Group 3 (triple-negative breast cancer) displayed increased TCA cycle intermediates, such as α-ketoglutarate and malate, reflecting a metabolic shift toward energy production and biosynthesis to sustain aggressive proliferation. In Group 4 (HER2-enriched), elevated phosphatidylcholines and phosphatidylethanolamines suggested upregulated mono-unsaturated phospholipid biosynthesis. The study provides a framework for leveraging multimodal data integration, attention-based feature selection, and self-organizing map analysis to identify biologically meaningful biomarkers.本研究整合了来自液相色谱-质谱联用（LC-MS）、气相色谱-质谱联用（GC-MS）以及核磁共振波谱（NMR）三种平台的多模态代谢组学数据，以系统性地识别区分乳腺癌亚型的生物标志物。一款基于前馈注意力机制的深度学习模型有效筛选出99种具有统计学意义的代谢物，其分类性能与生物标志物一致性均优于传统静态方法。通过融合多平台数据，该方法在保留生物学关联性的同时，获取了全面的代谢组学特征谱。自组织映射（Self-organizing map, SOM）分析揭示了各亚型独特的代谢特征，并明确了关键代谢通路。第1组（雌激素受体/孕激素受体阳性、人表皮生长因子受体2阴性）的丝氨酸、酪氨酸及2-氨基己二酸水平升高，提示其氨基酸代谢增强，可支持核苷酸合成与氧化还原稳态维持。第3组（三阴性乳腺癌）的三羧酸循环中间产物（如α-酮戊二酸与苹果酸）水平升高，反映出其代谢转向能量生成与生物合成，以支持侵袭性增殖。第4组（人表皮生长因子受体2富集型）的磷脂酰胆碱与磷脂酰乙醇胺水平升高，表明单不饱和磷脂生物合成通路被上调。本研究构建了一套可用于多模态数据整合、基于注意力机制的特征筛选以及自组织映射分析的研究框架，以识别具有生物学意义的生物标志物。