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Updated: Aug 21, 2026
Mass Cytometry Analysis of Systemic and Local Immune Responses in Hepatocellular Carcinoma
Published on: April 25, 2025
AI-integrated single-cell multi-omics decodes the hepatocellular carcinoma metabolism-immune axis: a new strategy for
Zihao Xu1, Yifan Liu1, Liangbin Cheng2
1School of Chinese Medicine, Hubei University of Chinese Medicine, Wuhan, Hubei, China.
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.
Insights
Artificial intelligence and multi-omics reveal how tumor metabolism drives immune suppression in liver cancer. This approach identifies key metabolic switches and guides the development of next-generation immunotherapies for hepatocellular carcinoma.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Hepatocellular carcinoma (HCC) treatment outcomes are limited by immune suppression from the tumor microenvironment.
- Single-cell RNA sequencing (scRNA-seq) reveals immune diversity but struggles to link cell states to location and metabolic drivers of dysfunction.
Purpose of the Study:
- To review how artificial intelligence (AI) and multi-omics can decode the metabolism-immunity network in HCC.
- To highlight metabolic pathways that impair anti-tumor immunity.
Main Methods:
- Integration of AI with single-cell and spatial multi-omics data.
- Analysis of metabolic switches, including lipid uptake affecting T cells and lactate impacting macrophages.
- Utilizing deep learning, spatial deconvolution, and foundation models for data integration and metabolic activity inference.
Main Results:
- Identification of key metabolic drivers of immune evasion in HCC.
- Prediction of actionable immune checkpoints based on metabolic insights.
Conclusions:
- AI-driven multi-omics transforms HCC research into predictive, mechanism-based models.
- Provides a roadmap for designing novel immunotherapies targeting the tumor metabolic environment.
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