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Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics
BACKGROUND: A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk predi...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618052/ https://www.ncbi.nlm.nih.gov/pubmed/26490766 http://dx.doi.org/10.1186/s13048-015-0195-6 |
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author | Zhang, Meiying Zhuang, Guanglei Sun, Xiangjun Shen, Yanying Zhao, Aimin Di, Wen |
author_facet | Zhang, Meiying Zhuang, Guanglei Sun, Xiangjun Shen, Yanying Zhao, Aimin Di, Wen |
author_sort | Zhang, Meiying |
collection | PubMed |
description | BACKGROUND: A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer. METHODS: A retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model. RESULTS: A total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS). CONCLUSIONS: Our synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13048-015-0195-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4618052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-46180522015-10-25 Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics Zhang, Meiying Zhuang, Guanglei Sun, Xiangjun Shen, Yanying Zhao, Aimin Di, Wen J Ovarian Res Research BACKGROUND: A high-quality risk prediction model is urgently needed for the clinical management of ovarian cancer. However most existing models are solely based on clinical parameters, and molecular classifications in recent reports are still being debated. This study aimed to establish a risk prediction model by using both clinicopathological and molecular factors (the synthetic model) for epithelial ovarian cancer. METHODS: A retrospective cohort study was conducted in epithelial ovarian cancer patients (n = 161) treated with primary debulking surgery and adjuvant chemotherapy. The expression level of 15 selected molecular markers were measured using immunohistochemistry. A risk model was developed using COX regression analysis with overall survival as the primary outcome. A simplified scoring system for each prognostic factor was based on its coefficient. Independent validation (n = 40) was conducted to evaluate the performance of the model. RESULTS: A total of 10 out of 15 molecular markers were significantly associated with clinical characteristics and overall survival. The synthetic model performed better than the clinicopathological risk model or the molecular risk model alone, as assessed by analysis of the receiver-operating characteristics curve area and the Youden index. The synthetic model included parity (>3), peritoneal metastasis, stage, tumor type, residual disease, and expression of human epidermal growth factor receptor 2 (HER2), epidermal growth factor receptor (EGFR), breast cancer 1 (BRCA1), murine sarcoma viral oncogene homolog B (BRAF) and Kirsten rat sarcoma viral oncogene homolog (KRAS). CONCLUSIONS: Our synthetic risk model may more accurately predict survival of epithelial ovarian cancer patients than current models. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13048-015-0195-6) contains supplementary material, which is available to authorized users. BioMed Central 2015-10-21 /pmc/articles/PMC4618052/ /pubmed/26490766 http://dx.doi.org/10.1186/s13048-015-0195-6 Text en © Zhang et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Zhang, Meiying Zhuang, Guanglei Sun, Xiangjun Shen, Yanying Zhao, Aimin Di, Wen Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title | Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title_full | Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title_fullStr | Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title_full_unstemmed | Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title_short | Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
title_sort | risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4618052/ https://www.ncbi.nlm.nih.gov/pubmed/26490766 http://dx.doi.org/10.1186/s13048-015-0195-6 |
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