Ovarian cancer (OC) is the second most common cancer of the female reproductive system. Due to the asymptomatic nature of early stages of OC and an increasingly poor prognosis in later stages, methods of screening for OC are much desired. Furthermore, screening and diagnosis processes, in order to justify use on asymptomatic patients, must be convenient and non-invasive. Recent developments in machine-learning technologies have made this possible via techniques in the field of metabolomics. The objective of this research was to use existing metabolomics data on OC and various analytic methods to develop a machine-learning model for the classification of potentially OC-related metabolite biomarkers. Pathway analysis and metabolite-set enrichment analysis were performed on gathered metabolite sets. Quantitative molecular descriptors were then used with various machine-learning classifiers for the diagnostics of OC using related metabolites. We elucidated that the metabolites associated with OC used for machine-learning models are involved in five metabolic pathways linked to OC: Nicotinate and Nicotinamide Metabolism, Glycolysis/Gluconeogenesis, Aminoacyl-tRNA Biosynthesis, Valine, Leucine and Isoleucine Biosynthesis, and Alanine, Aspartate and Glutamate Metabolism. Several classification models for the identification of OC using related metabolites were created and their accuracies were confirmed through testing with 10-fold cross-validation. The most accurate model was able to achieve 85.29% accuracy. The elucidation of biological pathways specific to OC using metabolic data and the observation of changes in these pathways in patients have the potential to contribute to the development of screening techniques for OC. Our results demonstrate the possibility of development of the machine-learning models for OC diagnostics using metabolomics data.

中文翻译：

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通过代谢物分析和机器学习诊断卵巢癌。

卵巢癌 (OC) 是女性生殖系统第二常见的癌症。由于 OC 早期阶段的无症状性质和后期阶段的预后越来越差，因此非常需要筛查 OC 的方法。此外，为了证明对无症状患者使用的合理性，筛查和诊断过程必须方便且无创。机器学习技术的最新发展通过代谢组学领域的技术使这成为可能。本研究的目的是使用现有的 OC 代谢组学数据和各种分析方法来开发机器学习模型，用于对潜在的 OC 相关代谢物生物标志物进行分类。对收集的代谢物集进行通路分析和代谢物集富集分析。然后将定量分子描述符与各种机器学习分类器一起使用，以使用相关代谢物诊断 OC。我们阐明了用于机器学习模型的与 OC 相关的代谢物涉及与 OC 相关的五个代谢途径：烟酸和烟酰胺代谢、糖酵解/糖异生、氨酰-tRNA 生物合成、缬氨酸、亮氨酸和异亮氨酸生物合成，以及丙氨酸、天冬氨酸和谷氨酸代谢。创建了多个使用相关代谢物鉴定 OC 的分类模型，并通过 10 倍交叉验证测试确认了它们的准确性。最准确的模型能够达到 85.29% 的准确率。使用代谢数据阐明 OC 特有的生物学途径以及观察患者这些途径的变化有可能有助于 OC 筛查技术的发展。我们的结果证明了使用代谢组学数据开发用于 OC 诊断的机器学习模型的可能性。