INTRODUCTION Endometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers. OBJECTIVES We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy. METHODS We screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection. RESULTS A total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively. CONCLUSION Phosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.

中文翻译：

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宫颈阴道液中的代谢组生物标志物，用于通过核磁共振波谱检测子宫内膜癌。

引言子宫内膜癌（EC）是发达国家中最常见的妇科肿瘤之一，但缺乏筛选生物标志物。目的我们旨在鉴定和验证宫颈阴道液（CVF）中的代谢组学生物标记物，以通过核磁共振（NMR）光谱法检测EC。方法我们筛选了100名怀疑患有EC和妇科良性疾病的妇女，并使用5：1研究设计将其随机分为训练和独立测试数据集。使用配备了低温探头的600 MHz NMR光谱仪分析了CVF样品。四种机器学习算法-支持向量机（SVM），偏最小二乘判别分析（PLS-DA），随机森林（RF）和逻辑回归（LR），应用于建立识别宫颈阴道液中代谢组学生物标志物以进行EC检测的模型。结果共有54名妇女符合最终分析的条件，其中21名EC和33名非EC。从宫颈阴道液样品中鉴定出的29种代谢产物中，选择通过SVM，RF和PLS-DA选择的独立代谢途径中存在的最高代谢产物，即磷胆碱，苹果酸和天冬酰胺，以建立预测模型。SVM，PLS-DA，RF和LR方法均在训练数据集中的曲线值下的0.88到0.92之间产生面积。在测试数据集中，SVM和RF方法的最高准确度分别为0.78，特异性为0.75和0.80。结论宫颈阴道液中的磷酸胆碱，天冬酰胺和苹果酸，