Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study
| العنوان: | Mass spectrometry and multivariate analysis to classify cervical intraepithelial neoplasia from blood plasma: an untargeted lipidomic study |
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| المؤلفون: | Kássio M. G. Lima, Ana Carolina de Oliveira Neves, Thais Pontes Pereira Mendes, Boniek G. Vaz, Camilo L. M. Morais |
| المصدر: | Scientific Reports, Vol 8, Iss 1, Pp 1-10 (2018) Scientific Reports |
| بيانات النشر: | Nature Publishing Group, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Adult, 0301 basic medicine, Oncology, medicine.medical_specialty, Support Vector Machine, Multivariate analysis, lcsh:Medicine, Cervical intraepithelial neoplasia, Malignancy, Sensitivity and Specificity, 01 natural sciences, Article, Mass Spectrometry, 03 medical and health sciences, Internal medicine, Lipidomics, medicine, Humans, lcsh:Science, F180, Cervical cancer, Principal Component Analysis, Multidisciplinary, business.industry, 010401 analytical chemistry, lcsh:R, Cancer, A300, Lipid Metabolism, Uterine Cervical Dysplasia, medicine.disease, Linear discriminant analysis, 0104 chemical sciences, Squamous intraepithelial lesion, 030104 developmental biology, Case-Control Studies, Multivariate Analysis, Female, lcsh:Q, business |
| الوصف: | Cervical cancer is still an important issue of public health since it is the fourth most frequent type of cancer in women worldwide. Much effort 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 specificity values of 80.0% and 83.3%, respectively. Five types of lipids contributing to the distinction between NILM and SIL classes were identified, including prostaglandins, phospholipids, and sphingolipids for the former condition and Tetranor-PGFM and hydroperoxide lipid for the latter. These findings highlight the potentiality of using mass spectrometry associated with chemometrics to discriminate between healthy women and those suffering from cervical pre-cancerous lesions. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2045-2322 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa2730abf9e1c13b19714c3e8c15cd78 http://link.springer.com/article/10.1038/s41598-018-22317-6 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....fa2730abf9e1c13b19714c3e8c15cd78 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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