{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T06:05:50Z","timestamp":1790057150385,"version":"4.0.1"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 \u201cneuro-immune-tumor-related\u201d pathways associated with LDS in breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Trial Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>http:\/\/itmctr.ccebtcm.org.cn\/<\/jats:ext-link>\n                      , identifier ITMCTR2025000446.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1873977","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T05:39:59Z","timestamp":1790055599000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression 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