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Multiple cancers have been reported to be associated with angiogenesis and are sensitive to anti-angiogenic therapies. Vascular normalization, by restoring proper tumor perfusion and oxygenation, could limit tumor cell invasiveness and improve the effectiveness of anticancer treatments. However, the underlying anticancer mechanisms of antiangiogenic drugs are still unknown. Metformin (MET) and simvastatin (SVA), two metabolic-related drugs, have been shown to play important roles in modulating the hypoxic tumor microenvironment and angiogenesis. Whether the combination of MET and SVA could exert a more effective antitumor effect than individual treatments has not been examined. The antitumor effect of the synergism of SVA and MET was detected in mouse models, breast cancer patient-derived organoids, and multiple tumor cell lines compared with untreated, SVA, or MET alone. RNA sequencing revealed that the combination of MET and SVA (but not MET or SVA alone) inhibited the expression of endothelin 1 (ET-1), an important regulator of angiogenesis and the hypoxia-related pathway. We demonstrate that the MET and SVA combination showed synergistic effects on inhibiting tumor cell proliferation, promoting apoptosis, alleviating hypoxia, decreasing angiogenesis, and increasing vessel normalization compared with the use of a single agent alone. The MET and SVA combination suppressed ET-1-induced hypoxia-inducible factor 1α expression by increasing prolyl hydroxylase 2 (PHD2) expression. Furthermore, the MET and SVA combination showed a more potent anticancer effect compared with bosentan. Together, our findings suggest the potential application of the MET and SVA combination in antitumor therapy.
Pubmed ID: 36156330 RIS Download
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THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role.
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View all literature mentionsCell line C-33 A is a Cancer cell line with a species of origin Homo sapiens (Human)
View all literature mentionsMus musculus with name C57BL/6J from IMSR.
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View all literature mentionsCell line SiHa is a Cancer cell line with a species of origin Homo sapiens
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