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Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings
Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focu...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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Bentham Science Publishers Ltd.
2007
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652404/ https://www.ncbi.nlm.nih.gov/pubmed/19384429 http://dx.doi.org/10.2174/138920207782446142 |
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author | Tinelli, Andrea Vergara, Daniele Martignago, Roberta Leo, Giuseppe Malvasi, Antonio Tinelli, Raffaele Marsigliante, Santo Maffia, Michele Lorusso, Vito |
author_facet | Tinelli, Andrea Vergara, Daniele Martignago, Roberta Leo, Giuseppe Malvasi, Antonio Tinelli, Raffaele Marsigliante, Santo Maffia, Michele Lorusso, Vito |
author_sort | Tinelli, Andrea |
collection | PubMed |
description | Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy. In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up. |
format | Text |
id | pubmed-2652404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | Bentham Science Publishers Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-26524042009-04-21 Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings Tinelli, Andrea Vergara, Daniele Martignago, Roberta Leo, Giuseppe Malvasi, Antonio Tinelli, Raffaele Marsigliante, Santo Maffia, Michele Lorusso, Vito Curr Genomics Article Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management. Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy. In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up. Bentham Science Publishers Ltd. 2007-08 /pmc/articles/PMC2652404/ /pubmed/19384429 http://dx.doi.org/10.2174/138920207782446142 Text en ©2007 Bentham Science Publishers Ltd. http://creativecommons.org/licenses/by/2.5/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.5/) which permits unrestrictive use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Article Tinelli, Andrea Vergara, Daniele Martignago, Roberta Leo, Giuseppe Malvasi, Antonio Tinelli, Raffaele Marsigliante, Santo Maffia, Michele Lorusso, Vito Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title | Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title_full | Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title_fullStr | Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title_full_unstemmed | Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title_short | Ovarian Cancer Biomarkers: A Focus on Genomic and Proteomic Findings |
title_sort | ovarian cancer biomarkers: a focus on genomic and proteomic findings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2652404/ https://www.ncbi.nlm.nih.gov/pubmed/19384429 http://dx.doi.org/10.2174/138920207782446142 |
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