Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome

Background Liver depression syndrome (LDS) is a core Chinese medicine (CM) syndrome implicated in the onset and progression of breast cancer, whose molecular mechanisms remain poorly understood, hindering further investigation. Methods We prospectively enrolled 146 participants and conducted transcriptomic, proteomic, and metabolomic analyses using peripheral blood samples. A graph-attention-based multi-omics model embedded with a Transformer-derived multi-head self-attention module served as an exploratory tool to integrate multi-omics datasets. The identified candidate molecules were subsequently validated via enzyme-linked immunosorbent assay (ELISA). Results In internal five-fold cross-validation, the Multi-Omics Graph Attention Network (MOGAT) achieved 96.6% accuracy in distinguishing LDS in breast cancer patients for biomarker-screening purposes, with proteomic features contributing predominantly to the classification. Moreover, both conventional multi-omics analyses and MOGAT analyses identified stimulator of interferon genes (STING), single immunoglobulin IL-1 receptor-related molecule (SIGIRR), tripartite motif-containing protein 56 (TRIM56), and anexelekto (AXL) as pivotal hub proteins, and subsequent ELISA validation confirmed differential expression of TRIM56 and AXL across pairwise comparisons. Multi-omics analysis identified noradrenaline as a candidate metabolite associated with BC-LDS related molecular alterations. The pathways underlying LDS in breast cancer were predominantly implicated in cytokine-cytokine receptor interactions, neuroactive ligand-receptor interaction, immune response regulation, and steroid hormone biosynthesis. These findings suggest candidate molecular features and a potential “neuro-immune-tumor-related” pathways associated with LDS in breast cancer. Conclusion This study examined the integration of multi-omics data with MOGAT to investigate the biological underpinnings of CM syndrome. These findings provide a preliminary paradigm that combines CM syndrome omics data with deep learning approaches, offering a hypothesis-generating framework for deciphering the scientific mechanisms of CM syndrome. Clinical Trial Registration http://itmctr.ccebtcm.org.cn/ , identifier ITMCTR2025000446.