Cheng, Shih-Chun published the artcileMetabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy, Computed Properties of 97-67-6, the main research area is metabolome biomarker cervicovaginal fluid endometrial cancer NMR spectroscopy; Biomarkers; Endometrial neoplasms; Magnetic resonance spectroscopy; Metabolomics.
Endometrial cancer (EC) is one of the most common gynecol. neoplasms in developed countries but lacks screening biomarkers. We aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through NMR (NMR) spectroscopy. We screened 100 women with suspicion of EC and benign gynecol. 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 anal. (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. A total of 54 women were eligible for the final anal., 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, resp. 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.
Metabolomics published new progress about Algorithm. 97-67-6 belongs to class alcohols-buliding-blocks, name is (S)-2-hydroxysuccinic acid, and the molecular formula is C4H6O5, Computed Properties of 97-67-6.
Referemce:
Alcohol – Wikipedia,
Alcohols – Chemistry LibreTexts