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Yijun Wang

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Open access Jul 2026

Hypoxia tumor-associated macrophages facilitate hepatocellular carcinoma metastasis via uPA-uPAR pathway.

BACKGROUND Hepatocellular carcinoma (HCC) arises within a hypoxic and immunosuppressive tumor microenvironment (TME), where tumor-associated macrophages (TAMs) constitute a major immune population. The impact of hypoxia on TAM functional heterogeneity and their contribution to HCC growth and metastasis remain incompletely understood. METHODS We integrated multiple single-cell RNA sequencing datasets and developed a machine learning framework to map cellular hypoxia at single-cell resolution. Hypoxic TAMs (H-TAM) were characterized using transcriptomic and functional assays, including pseudotime trajectory analysis, regulatory network inference, in vitro co-culture, and orthotopic mouse models. Drug sensitivity correlations and in vivo validation were performed to evaluate therapeutic strategies. RESULTS H-TAM represented the most hypoxic immune population in HCC and exhibited enhanced interactions with malignant hepatocytes. Transcriptomic profiling revealed HIF-1α-dependent hypoxia signaling and upregulation of plasminogen activator, urokinase (uPA, encoded by PLAU). H-TAM-derived uPA engaged its receptor uPAR on HCC cells, promoting epithelial-mesenchymal transition (EMT), migration, invasion, and lung metastasis. In line with this, genetic silencing of the corresponding mouse gene Plau in hypoxia-exposed bone marrow-derived macrophages (H-BMDM) markedly attenuated these pro-tumorigenic effects. Integrative drug sensitivity analysis identified dasatinib as a potential therapeutic agent in HCC with high uPAR expression, and in vivo administration selectively suppressed H-BMDM mediated tumor progression and metastasis while prolonging mouse survival. CONCLUSIONS Hypoxia drives TAM heterogeneity in HCC via an H‑TAM‑intrinsic HIF‑1α-uPA axis that engages uPAR on HCC cells to promote metastasis. uPAR is a potential prognostic biomarker, and dasatinib is a promising therapy to block this axis and improve outcomes.

Yijun Wang, Xiaomin You, Xiangyuan Luo et al. · 0 citations