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A Multi-Omics Study Revealing the Metabolic Effects of Estrogen in Liver Cancer Cells HepG2.

Minqian ShenMengyang XuFanyi ZhongMcKenzie C CristAnjali B PriorKundi YangDanielle M AllaireFouad ChoueiryJiangjiang ZhuHaifei Shi
Published in: Cells (2021)
Hepatocellular carcinoma (HCC) that is triggered by metabolic defects is one of the most malignant liver cancers. A much higher incidence of HCC among men than women suggests the protective roles of estrogen in HCC development and progression. To begin to understand the mechanisms involving estrogenic metabolic effects, we compared cell number, viability, cytotoxicity, and apoptosis among HCC-derived HepG2 cells that were treated with different concentrations of 2-deoxy-d-glucose (2-DG) that blocks glucose metabolism, oxamate that inhibits lactate dehydrogenase and glycolysis, or oligomycin that blocks ATP synthesis and mitochondrial oxidative phosphorylation. We confirmed that HepG2 cells primarily utilized glycolysis followed by lactate fermentation, instead of mitochondrial oxidative phosphorylation, for cell growth. We hypothesized that estrogen altered energy metabolism via its receptors to carry out its anticancer effects in HepG2 cells. We treated cells with 17β-estradiol (E2), 1,3,5-tris(4-hydroxyphenyl)-4-propyl-1H-pyrazole (PPT) an estrogen receptor (ER) α (ERα) agonist, or 2,3-bis(4-hydroxyphenyl)-propionitrile (DPN), an ERβ agonist. We then used transcriptomic and metabolomic analyses and identified differentially expressed genes and unique metabolite fingerprints that are produced by each treatment. We further performed integrated multi-omics analysis, and identified key genes and metabolites in the gene-metabolite interaction contributed by E2 and ER agonists. This integrated transcriptomic and metabolomic study suggested that estrogen acts on estrogen receptors to suppress liver cancer cell growth via altering metabolism. This is the first exploratory study that comprehensively investigated estrogen and its receptors, and their roles in regulating gene expression, metabolites, metabolic pathways, and gene-metabolite interaction in HCC cells using bioinformatic tools. Overall, this study provides potential therapeutic targets for future HCC treatment.
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