卵巢癌是一种侵袭性、异质性疾病，诊断较晚且对化疗产生耐药性。卵巢癌的临床特征可以通过研究其代谢以及特定途径的调节如何与个体表型联系起来来解释。卵巢癌因其异质性而受到代谢研究的特别关注，已鉴定出五种不同的亚型，每种亚型都可能表现出独特的代谢特征。为了阐明代谢差异，基于约束的建模（CBM）代表了一种强大的技术，可以整合“组学”数据，例如转录组学。然而，许多CBM方法并没有优先考虑准确的生长率预测，而且卵巢癌基因组规模的研究也很少。这里，开发了一种新的 CBM 方法，采用基因组规模模型 Human1 和通量平衡分析，能够整合体外生长速率、转录组数据和培养基条件来预测细胞的代谢行为。使用低级别和高级别卵巢癌，可以预测亚型特异性代谢差异，这得到了来自癌细胞系百科全书的公开 CRISPR-Cas9 数据和广泛的文献综述的支持。已经提出了侵袭性、侵袭性表型的代谢驱动因素，以及导致低级细胞系化疗耐药性增加的途径。实验基因依赖性数据已用于验证磷酸戊糖途径区域对于低级细胞生长至关重要，突显了这种卵巢癌亚型的潜在脆弱性。© 2024。作者。

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.© 2024. The Author(s).