ReviewFrontiers in immunology2026
Decoding the spatial logic of immune evasion in hepatocellular carcinoma: from single-cell profiling to spatial omics.
Review in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Hepatocellular carcinoma remains difficult to treat because immune resistance is organized not only by malignant cell states but also by the spatial architecture of the tumor microenvironment. Single-cell RNA sequencing has clarified cellular heterogeneity in liver cancer, yet tissue dissociation removes the positional relationships that determine whether immune cells can reach and eliminate tumor nests. Spatial transcriptomic, proteomic, and multiplex imaging technologies now make it possible to map immune exclusion, stromal barriers, vascular gatekeeping, tertiary lymphoid structures, macrophage and fibroblast programs, and metabolic checkpoints within intact tumor tissue. This review integrates recent single-cell and spatial multi-omics evidence to explain how suppressive neighborhoods emerge in hepatocellular carcinoma and how they shape immunotherapy response. We discuss the mechanisms through which malignant, myeloid, lymphoid, endothelial, and stromal populations interact across spatial niches, and we examine how these maps can inform biomarker discovery, patient stratification, and rational combination therapy. We also highlight limitations in cohort design, sampling depth, analytical integration, and clinical validation that currently constrain translation. By linking cellular states to their tissue coordinates, spatial omics provides a framework for understanding why ICI succeed in some tumors but fail in others. Integrating single-cell resolution with spatial context may help convert descriptive tumor atlases into clinically actionable maps for precision immuno-oncology.
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