Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study
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2018-03-02
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Cervical cancer is still an important issue of public health since it is the fourth most frequent type of
cancer in women worldwide. Much efort has been dedicated to combating this cancer, in particular
by the early detection of cervical pre-cancerous lesions. For this purpose, this paper reports the use
of mass spectrometry coupled with multivariate analysis as an untargeted lipidomic approach to
classifying 76 blood plasma samples into negative for intraepithelial lesion or malignancy (NILM,
n=42) and squamous intraepithelial lesion (SIL, n=34). The crude lipid extract was directly analyzed
with mass spectrometry for untargeted lipidomics, followed by multivariate analysis based on the
principal component analysis (PCA) and genetic algorithm (GA) with support vector machines (SVM),
linear (LDA) and quadratic (QDA) discriminant analysis. PCA-SVM models outperformed LDA and QDA
results, achieving sensitivity and specifcity values of 80.0% and 83.3%, respectively. Five types of lipids
contributing to the distinction between NILM and SIL classes were identifed, including prostaglandins,
phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid
for the latter. These fndings highlight the potentiality of using mass spectrometry associated with
chemometrics to discriminate between healthy women and those sufering from cervical pre-cancerous
lesions.
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NEVES, Ana C. O. et al. Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study. Scientific Reports, London, v. 8, e3954, 2018. DOI: 10.1038/s41598-018-22317-6. Disponível em: https://www.nature.com/articles/s41598-018-22317-6. Acesso em: 21 jun. 2023.