Abstract
The extensive intratumoral and microenvironmental heterogeneity of hepatocellular carcinoma (HCC) remains a major therapeutic barrier. Integrating single-cell transcriptomics samples spanning normal liver, primary tumors, portal vein tumor thrombus (PVTT), and metastatic lymph nodes (MLN) with spatial profiling, we systematically dissected cellular ecosystems driving HCC progression. Malignant hepatocytes segregated into four transcriptional meta-programs with divergent clinical trajectories: Diff-Metabolic, Prolif-Stress, MYC-Biosynth-Immune, and EMT-Inflammatory states. Diff-Metabolic cells retained liver-specific functions with favorable prognosis, whereas the other three programs correlated with disease advancement; notably, all four states exhibited differential therapeutic vulnerabilities, including sorafenib resistance. Within the tumor microenvironment, immunosuppressive Macro-SPP1 and Macro-TREM2 populations expanded during tumor progression. Spatial mapping revealed organized stromal territories where Endo-ESM1 endothelial cells and Fib-POSTN/Fib-CD36 fibroblasts establish TGFβ-enriched niches spatially correlating with Prolif-Stress and EMT-Inflammatory tumor cells, linking stromal architecture to malignant phenotypes. Endothelial-fibroblast crosstalk intensified through extracellular matrix and angiogenic signaling during progression. Geneformer-based virtual knockout screening identified HSP90B1 as a convergent dependency, validated by its cancer cell essentiality, HCC overexpression, abundance in treatment-resistant tumors, and association with adverse survival. This integrated atlas establishes a framework for targeting tumor-intrinsic states and microenvironmental dependencies in HCC.
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Data availability
Single-cell RNA-seq data analyzed in this study are available from public repositories under accession numbers GSE125449, GSE149614, GSE151530, GSE156625, GSE134355, and GSE189903 in GEO database. Spatial transcriptomics data are available at http://lifeome.net/supp/livercancer-st/data.htm and GSE238264. Bulk RNA-seq data were obtained from GSE109211 and TCGA-LIHC (https://portal.gdc.cancer.gov/). Source data and additional information supporting the findings of this study are available within the article and its Supplementary Information files. Custom analysis code and all other data are available from the corresponding author upon reasonable request.
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Acknowledgements
This work was financially supported by National Natural Science Foundation of China (No. 82471640, 82573229), National Key Research and Development Program of China (2021YFF0702400) and Gansu Provincial Natural Science Foundation Specialized Project on Laboratory Animals (25JRRA725), and the Fundamental Research Funds for the Central Universities (lzujbky-2025-it39). This research work is supported by the Supercomputing Center of Lanzhou University.
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P.X., S.S., and D.F. contributed equally as co-first authors to this work. P.X., S.S., and D.F. designed the study, analyzed data, and wrote the original draft. P.X. and L.L. developed the computational methodology and performed bioinformatics analyses. D.Y., Y.G., P.J., and X.Y. assisted with data collection and curation. Yixiao T. contributed to the literature review and manuscript editing. Yingxia T., R.S., and D.W. supervised the study, conceptualized the project, acquired funding, critically reviewed and revised the manuscript, and approved the final version. All authors reviewed and approved the final manuscript.
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Xia, P., Shuang, S., Fu, D. et al. Large-scale single-cell analysis and in silico perturbation reveal dynamic evolution of HCC: from initiation to therapeutic targeting. npj Precis. Onc. (2026). https://doi.org/10.1038/s41698-026-01307-2
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DOI: https://doi.org/10.1038/s41698-026-01307-2
