AI-integrated single-cell multi-omics decodes the hepatocellular carcinoma metabolism-immune axis: a new strategy for precision therapeutic targeting.

BACKGROUND

Immune checkpoint inhibitors have improved outcomes for hepatocellular carcinoma, yet most patients do not respond because the tumor's 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.

METHODS

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.

RESULTS

These approaches enable researchers to identify key metabolic drivers of immune evasion and predict which checkpoints are most actionable.

CONCLUSIONS

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.