{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T10:51:41Z","timestamp":1785927101354,"version":"3.56.0"},"reference-count":113,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Immune checkpoint inhibitors have improved outcomes for hepatocellular carcinoma, yet most patients do not respond because the tumor\u2019s metabolic environment suppresses immune cells. Single-cell RNA sequencing has revealed extensive immune diversity, but conventional analyses cannot link cell states to their physical location or to the metabolic signals that drive dysfunction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this review, we examine how artificial intelligence, combined with single-cell and spatial multi-omics, can decode the metabolism-immunity network in liver cancer. We highlight two key metabolic switches: lipid uptake through a scavenger receptor that triggers ferroptosis in killer T cells, and lactate-induced changes in gene regulation that lock macrophages into a tumor-promoting state. We also summarize advanced computational tools including deep learning for data integration, spatial deconvolution, and foundation models that can infer metabolic activity from single-cell data and reconstruct cell movement over time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>These approaches enable researchers to identify key metabolic drivers of immune evasion and predict which checkpoints are most actionable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Artificial-intelligence-driven multi-omics transforms hepatocellular carcinoma research from descriptive catalogues into predictive, mechanism-based models, offering a roadmap for designing next-generation immunotherapies.<\/jats:p>\n                  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