Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor–host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

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代谢组学在卵巢癌诊断中的新兴作用

由于缺乏明确的症状和有效的诊断工具（通常导致晚期诊断），卵巢癌被认为是沉默的杀手。近年来，对熟练生物标志物的迫切需求促使研究人员考虑了代谢组学，这是一种新兴的组学科学，致力于分析生物系统中存在的整个小分子（≤1.5kDa）。已经发现，代谢组学谱可作为肿瘤与宿主相互作用的镜像，可用于分析和鉴定特定的癌症表型。癌症可能会引起重大的代谢改变以维持其生长，而代谢组学可能会突出这一点，从而有可能在发展的早期阶段检测出癌症。在过去的十年中 代谢组学已被广泛应用于识别不同的代谢特征，以改善卵巢癌的诊断。这篇综述的目的是为了更新代谢组学研究的现状，以发现新的卵巢癌诊断代谢组学生物标志物。鉴于其潜在的生物学意义，讨论了最有前途的代谢改变，这些问题限制了将其有效地临床翻译为卵巢癌诊断工具的问题。