Author(s):
Nunes, Sofia C. ; Sousa, Joana ; Silva, Fernanda ; Silveira, Margarida ; Guimarães, António ; Serpa, Jacinta ; Félix, Ana ; Gonçalves, Luís G.
Date: 2023
Persistent ID: http://hdl.handle.net/10362/159814
Origin: Repositório Institucional da UNL
Subject(s): benign ovarian tumors; biomarkers; borderline ovarian tumors; cancer progression; malignant ovarian tumors; NMR metabolomics; ovarian cancer; serum; Endocrinology, Diabetes and Metabolism; Biochemistry; Molecular Biology; SDG 3 - Good Health and Well-being
Description
Funding Information: The institutions are funded by Fundação para a Ciência e a Tecnologia/Ministério da Ciência, Tecnologia e Ensino Superior (FCT/MCTES, Portugal) through national funds to iNOVA4Health (UIDB/04462/2020 and UIDP/04462/2020), to MOSTMICRO-ITQB (UIDB/04612/2020 and UIDP/04612/2020) and the Associated Laboratory LS4FUTURE (LA/P/0087/2020). The NMR data was acquired at CERMAX, ITQB-NOVA, Oeiras, Portugal with equipment funded by FCT, project AAC 01/SAICT/2016 and at CICS-UBI (Health Sciences Research Centre—University of Beira Interior) with equipment acquired in the scope of the project POVT-0439-FEDER-00001. Luis G. Gonçalves was financed by a FCT contract according to DL57/2016, [SFRH/BPD/111100/2015]. Publisher Copyright: © 2023 by the authors.
Ovarian cancer is the major cause of death from gynecological cancer and the third most common gynecological malignancy worldwide. Despite a slight improvement in the overall survival of ovarian carcinoma patients in recent decades, the cure rate has not improved. This is mainly due to late diagnosis and resistance to therapy. It is therefore urgent to develop effective methods for early detection and prognosis. We hypothesized that, besides being able to distinguish serum samples of patients with ovarian cancer from those of patients with benign ovarian tumors, 1H-NMR metabolomics analysis might be able to predict the malignant potential of tumors. For this, serum 1H-NMR metabolomics analyses were performed, including patients with malignant, benign and borderline ovarian tumors. The serum metabolic profiles were analyzed by multivariate statistical analysis, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. A metabolic profile associated with ovarian malignant tumors was defined, in which lactate, 3-hydroxybutyrate and acetone were increased and acetate, histidine, valine and methanol were decreased. Our data support the use of 1H-NMR metabolomics analysis as a screening method for ovarian cancer detection and might be useful for predicting the malignant potential of borderline tumors.
