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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Vydané v:	PloS one Ročník 16; číslo 8; s. e0255804
Hlavní autori:	Cirillo, Priscila D. R., Margiotti, Katia, Fabiani, Marco, Barros-Filho, Mateus C., Sparacino, David, Cima, Antonella, Longo, Salvatore A., Cupellaro, Marina, Mesoraca, Alvaro, Giorlandino, Claudio
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 05.08.2021 Public Library of Science (PLoS)
Predmet:	Adult Aged Analysis Biology and life sciences Biomarkers, Tumor - blood Biomarkers, Tumor - genetics Cancer Case-Control Studies Diagnosis Early Detection of Cancer - methods Female Gene expression Humans Medicine and Health Sciences MicroRNA MicroRNAs - blood MicroRNAs - genetics Middle Aged Ovarian cancer Ovarian Neoplasms - blood Ovarian Neoplasms - diagnosis Ovarian Neoplasms - genetics Research and Analysis Methods Italy
ISSN:	1932-6203, 1932-6203
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Giorlandino, Claudio Mesoraca, Alvaro Cima, Antonella Cupellaro, Marina Fabiani, Marco Cirillo, Priscila D. R. Sparacino, David Barros-Filho, Mateus C. Longo, Salvatore A. Margiotti, Katia
AuthorAffiliation	3 Altamedica, Department of Biochemistry, Altamedica Main Centre, Rome, Italy 2 Department of Head and Neck Surgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil 1 Altamedica Center, Human Genetics Laboratories, Altamedica Main Center, Rome, Italy 4 Altamedica, Department of Prenatal Diagnosis, Fetal-Maternal Medical Center, Rome, Italy Gustave Roussy, FRANCE
AuthorAffiliation_xml	– name: 2 Department of Head and Neck Surgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brazil – name: 1 Altamedica Center, Human Genetics Laboratories, Altamedica Main Center, Rome, Italy – name: 3 Altamedica, Department of Biochemistry, Altamedica Main Centre, Rome, Italy – name: Gustave Roussy, FRANCE – name: 4 Altamedica, Department of Prenatal Diagnosis, Fetal-Maternal Medical Center, Rome, Italy
Author_xml	– sequence: 1 givenname: Priscila D. R. surname: Cirillo fullname: Cirillo, Priscila D. R. – sequence: 2 givenname: Katia orcidid: 0000-0003-0432-1525 surname: Margiotti fullname: Margiotti, Katia – sequence: 3 givenname: Marco orcidid: 0000-0002-8763-0114 surname: Fabiani fullname: Fabiani, Marco – sequence: 4 givenname: Mateus C. surname: Barros-Filho fullname: Barros-Filho, Mateus C. – sequence: 5 givenname: David surname: Sparacino fullname: Sparacino, David – sequence: 6 givenname: Antonella surname: Cima fullname: Cima, Antonella – sequence: 7 givenname: Salvatore A. surname: Longo fullname: Longo, Salvatore A. – sequence: 8 givenname: Marina surname: Cupellaro fullname: Cupellaro, Marina – sequence: 9 givenname: Alvaro surname: Mesoraca fullname: Mesoraca, Alvaro – sequence: 10 givenname: Claudio surname: Giorlandino fullname: Giorlandino, Claudio
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: PDRC, KM,MF,MCBF,DS,AC,SAL,MC, and AM are employed by Altamedica Medical centre, and CG is the scientific director of Altamedica Medical centre of Rome. There are no patents, products in development or market products to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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Snippet	Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are...
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SubjectTerms	Adult Aged Analysis Biology and life sciences Biomarkers, Tumor - blood Biomarkers, Tumor - genetics Cancer Case-Control Studies Diagnosis Early Detection of Cancer - methods Female Gene expression Humans Medicine and Health Sciences MicroRNA MicroRNAs - blood MicroRNAs - genetics Middle Aged Ovarian cancer Ovarian Neoplasms - blood Ovarian Neoplasms - diagnosis Ovarian Neoplasms - genetics Research and Analysis Methods
Title	Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection
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