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Abstract

Hypoxia, a hallmark of solid tumors, promotes the malignant progression and is challenging to target. Metabolic reprogramming and the resulting metabolic vulnerabilities provide a promising strategy to target tumor hypoxia. Here we systematically compared the metabolic network differences between hypoxic and non-hypoxic cells, and developed a deep learning model, "DepFormer", to predict the dependent metabolic genes in hypoxic tumor cells. The performance of DepFormer was validated using CRISPR screening dataset. Oxidative phosphorylation was identified as the most significantly hypoxia-dependent metabolic pathway, and FLAD1 was predicted to be one of the key hypoxia-dependent metabolic genes. FLAD1 locus is amplified, and FLAD1 expression is upregulated in various tumor types, especially in hypoxic tumors. FLAD1 depletion compromises tumors adaptation to hypoxia by disrupting mitochondrial complex II activity, leading to an imbalance between succinate and fumarate, and consequent failure to adapt to hypoxia. Subsequently, we identified a drug-like inhibitor of FLAD1, which selectively inhibits the growth of tumor cells under hypoxia. Our findings reveal FLAD1 as an innovative therapeutic target for hypoxic tumors.

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02 /Specification & Classification

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