Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.

Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized...

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Autores principales:	Priscila D R Cirillo, Katia Margiotti, Marco Fabiani, Mateus C Barros-Filho, David Sparacino, Antonella Cima, Salvatore A Longo, Marina Cupellaro, Alvaro Mesoraca, Claudio Giorlandino
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bc722f20ee0b4b148fd09ece59a633c1
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spelling	oai:doaj.org-article:bc722f20ee0b4b148fd09ece59a633c12021-12-02T20:18:36ZMulti-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.1932-620310.1371/journal.pone.0255804https://doaj.org/article/bc722f20ee0b4b148fd09ece59a633c12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255804https://doaj.org/toc/1932-6203Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.Priscila D R CirilloKatia MargiottiMarco FabianiMateus C Barros-FilhoDavid SparacinoAntonella CimaSalvatore A LongoMarina CupellaroAlvaro MesoracaClaudio GiorlandinoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255804 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Priscila D R Cirillo Katia Margiotti Marco Fabiani Mateus C Barros-Filho David Sparacino Antonella Cima Salvatore A Longo Marina Cupellaro Alvaro Mesoraca Claudio Giorlandino Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
description	Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.
format	article
author	Priscila D R Cirillo Katia Margiotti Marco Fabiani Mateus C Barros-Filho David Sparacino Antonella Cima Salvatore A Longo Marina Cupellaro Alvaro Mesoraca Claudio Giorlandino
author_facet	Priscila D R Cirillo Katia Margiotti Marco Fabiani Mateus C Barros-Filho David Sparacino Antonella Cima Salvatore A Longo Marina Cupellaro Alvaro Mesoraca Claudio Giorlandino
author_sort	Priscila D R Cirillo
title	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
title_short	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
title_full	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
title_fullStr	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
title_full_unstemmed	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.
title_sort	multi-analytical test based on serum mirnas and proteins quantification for ovarian cancer early detection.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/bc722f20ee0b4b148fd09ece59a633c1
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Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection.

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