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Alterations of cellular metabolism play a central role in the development and progression of cancer. Oroxylin A, an active flavonoid of a Chinese traditional medicinal plant, was previously shown to modulate glycolysis in cancer cells. However, the mechanism by which oroxylin A regulates glycolysis is still not well defined. Here, we show that oroxylin A inhibits glycolysis in breast cancer cells via the Sirtuin 3 (SIRT3)-mediated destabilization of hypoxia-inducible factor 1α (HIF1α), which controls glycolytic gene expression. Oroxylin A promotes superoxide dismutase (SOD2) gene expression through SIRT3-regulated DNA-binding activity of FOXO3a and increases the activity of SOD2 by promoting SIRT3-mediated deacetylation. In vivo, oroxylin A inhibits the growth of transplanted human breast tumors associated with glycolytic suppression. These data indicate that oroxylin A inhibits glycolysis-dependent proliferation of breast cancer cells, through the suppression of HIF1α stabilization via SIRT3 activation, providing preclinical information for the cancer therapies of SIRT3 stimulation.

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Background Pituitary neuroendocrine tumors (PitNETs) are the second most common intracranial tumor. We lacked a comprehensive understanding of the pathogenesis and heterogeneity of these tumors. Methods We performed high-precision single-cell RNA sequencing for 2679 individual cells obtained from 23 surgically resected samples of the major subtypes of PitNETs from 21 patients. We also performed single-cell multi-omics sequencing for 238 cells from 5 patients. Results Unsupervised clustering analysis distinguished all tumor subtypes, which was in accordance with the classification based on immunohistochemistry and provided additional information. We identified 3 normal endocrine cell types: somatotrophs, lactotrophs, and gonadotrophs. Comparisons of tumor and matched normal cells showed that differentially expressed genes of gonadotroph tumors were predominantly downregulated, while those of somatotroph and lactotroph tumors were mainly upregulated. We identified novel tumor-related genes, such as AMIGO2, ZFP36, BTG1, and DLG5. Tumors expressing multiple hormone genes showed little transcriptomic heterogeneity. Furthermore, single-cell multi-omics analysis demonstrated that the tumor had a relatively uniform pattern of genome with slight heterogeneity in copy number variations. Conclusions Our single-cell transcriptome and single-cell multi-omics analyses provide novel insights into the characteristics and heterogeneity of these complex neoplasms for the identification of biomarkers and therapeutic targets.