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Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma
BACKGROUND: In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requ...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109044/ https://www.ncbi.nlm.nih.gov/pubmed/32037399 http://dx.doi.org/10.1038/s41416-020-0745-6 |
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author | Berg, Hege F. Ju, Zhenlin Myrvold, Madeleine Fasmer, Kristine E. Halle, Mari K. Hoivik, Erling A. Westin, Shannon N. Trovik, Jone Haldorsen, Ingfrid S. Mills, Gordon B. Krakstad, Camilla Werner, Henrica M. J. |
author_facet | Berg, Hege F. Ju, Zhenlin Myrvold, Madeleine Fasmer, Kristine E. Halle, Mari K. Hoivik, Erling A. Westin, Shannon N. Trovik, Jone Haldorsen, Ingfrid S. Mills, Gordon B. Krakstad, Camilla Werner, Henrica M. J. |
author_sort | Berg, Hege F. |
collection | PubMed |
description | BACKGROUND: In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy. METHODS: Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing. RESULTS: LNM was predicted with area under the curve 0.72–0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype. CONCLUSIONS: We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC. |
format | Online Article Text |
id | pubmed-7109044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-71090442021-02-10 Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma Berg, Hege F. Ju, Zhenlin Myrvold, Madeleine Fasmer, Kristine E. Halle, Mari K. Hoivik, Erling A. Westin, Shannon N. Trovik, Jone Haldorsen, Ingfrid S. Mills, Gordon B. Krakstad, Camilla Werner, Henrica M. J. Br J Cancer Article BACKGROUND: In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis (LNM), leading to both under- and over-treatment. We aimed to develop models that integrate protein data with clinical information to identify patients requiring more aggressive surgery, including lymphadenectomy. METHODS: Protein expression profiles were generated for 399 patients using reverse-phase protein array. Three generalised linear models were built on proteins and clinical information (model 1), also with magnetic resonance imaging included (model 2), and on proteins only (model 3), using a training set, and tested in independent sets. Gene expression data from the tumours were used for confirmatory testing. RESULTS: LNM was predicted with area under the curve 0.72–0.89 and cyclin D1; fibronectin and grade were identified as important markers. High levels of fibronectin and cyclin D1 were associated with poor survival (p = 0.018), and with markers of tumour aggressiveness. Upregulation of both FN1 and CCND1 messenger RNA was related to cancer invasion and mesenchymal phenotype. CONCLUSIONS: We demonstrate that data-driven prediction models, adding protein markers to clinical information, have potential to significantly improve preoperative identification of patients with LNM in EEC. Nature Publishing Group UK 2020-02-10 2020-03-31 /pmc/articles/PMC7109044/ /pubmed/32037399 http://dx.doi.org/10.1038/s41416-020-0745-6 Text en © The Author(s), under exclusive licence to Cancer Research UK 2020 https://creativecommons.org/licenses/by/4.0/Note This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0). |
spellingShingle | Article Berg, Hege F. Ju, Zhenlin Myrvold, Madeleine Fasmer, Kristine E. Halle, Mari K. Hoivik, Erling A. Westin, Shannon N. Trovik, Jone Haldorsen, Ingfrid S. Mills, Gordon B. Krakstad, Camilla Werner, Henrica M. J. Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title | Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title_full | Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title_fullStr | Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title_full_unstemmed | Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title_short | Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
title_sort | development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109044/ https://www.ncbi.nlm.nih.gov/pubmed/32037399 http://dx.doi.org/10.1038/s41416-020-0745-6 |
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