Identification and Validation of Metabolomic Biomarkers in Cervicovaginal Fluid for Detecting Endometrial Cancer Through Nuclear Magnetic Resonance Spectroscopym

IntroductionEndometrial 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.
MethodsWe screened 200 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testingdatasets 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.
ResultsAtotal of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, we selected phosphocholine, asparagine, and malate 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.
ConclusionPhosphocholine, 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.