Ha Ji Hee, Jayaraman Muralidharan, Nadhan Revathy, Kashyap Srishti, Mukherjee Priyabrata, Isidoro Ciro, Song Yong Sang, Dhanasekaran Danny N
Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
Department of Cell Biology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
Biomedicines. 2021 Dec 16;9(12):1927. doi: 10.3390/biomedicines9121927.
Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.
聚焦于定义卵巢癌中基于代谢物的肿瘤间异质性，我们使用一个可检测731种化合物的代谢组学平台，研究了一组高级别浆液性卵巢癌（HGSOC）细胞系的代谢多样性。通过代谢指纹分析，随后进行二维和三维主成分分析，与输卵管上皮细胞系对照相比，将HGSOC细胞聚类为五个不同的代谢组，从而确定了HGSOC细胞的异质性。在大多数癌细胞中观察到与有氧糖酵解和磷脂代谢相关的代谢物总体增加。还观察到参与转硫作用和谷胱甘肽合成的代谢物水平显著增加。更显著的是，HGSOC细胞亚群显示5-羟色胺、γ-氨基丁酸或谷氨酸水平升高。此外，5-羟色胺合成抑制剂以及γ-氨基丁酸和谷氨酸受体拮抗剂可抑制HGSOC细胞的增殖，表明它们在癌细胞中作为肿瘤代谢物和受体介导的自分泌信号配体的潜在作用。与此作用一致，5-羟色胺合成抑制剂以及γ-氨基丁酸和谷氨酸受体拮抗剂抑制了HGSOC细胞的增殖。这些拮抗剂还抑制了代表性HGSOC细胞系TYKNU细胞的三维球体生长。这些结果确定5-羟色胺、γ-氨基丁酸和谷氨酸为卵巢癌代谢亚型中的假定肿瘤代谢物，并指出它们是基于代谢指纹分析的治疗策略中的治疗靶点。
