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Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model
BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We a...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528212/ https://www.ncbi.nlm.nih.gov/pubmed/31109334 http://dx.doi.org/10.1186/s12967-019-1915-2 |
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author | Cheng, Sen Wu, Jiaqi Li, Chuzhong Li, Yangfang Liu, Chunhui Li, Guilin Li, Wuju Hu, Shuofeng Ying, Xiaomin Zhang, Yazhuo |
author_facet | Cheng, Sen Wu, Jiaqi Li, Chuzhong Li, Yangfang Liu, Chunhui Li, Guilin Li, Wuju Hu, Shuofeng Ying, Xiaomin Zhang, Yazhuo |
author_sort | Cheng, Sen |
collection | PubMed |
description | BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1915-2) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6528212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65282122019-05-28 Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model Cheng, Sen Wu, Jiaqi Li, Chuzhong Li, Yangfang Liu, Chunhui Li, Guilin Li, Wuju Hu, Shuofeng Ying, Xiaomin Zhang, Yazhuo J Transl Med Research BACKGROUND: Compared with clinically functioning pituitary adenoma (FPA), clinically non-functioning pituitary adenoma (NFPA) lacks of detectable hypersecreting serum hormones and related symptoms which make it difficult to predict the prognosis and monitoring for postoperative tumour regrowth. We aim to investigate whether the expression of selected tumour-related proteins and clinical features could be used as tumour markers to effectively predict the regrowth of NFPA. METHOD: Tumour samples were collected from 295 patients with NFPA from Beijing Tiantan Hospital. The expression levels of 41 tumour-associated proteins were assessed using tissue microarray analyses. Clinical characteristics were analysed via univariate and multivariate logistic regression analyses. Logistic regression algorithm was applied to build a prediction model based on the expression levels of selected proteins and clinical signatures, which was then assessed in the testing set. RESULTS: Three proteins and two clinical signatures were confirmed to be significantly related to the regrowth of NFPA, including cyclin-dependent kinase inhibitor 2A (CDKN2A/p16), WNT inhibitory factor 1 (WIF1), tumour growth factor beta (TGF-β), age and tumour volume. A prediction model was generated on the training set, which achieved a fivefold predictive accuracy of 81.2%. The prediction ability was validated on the testing set with an accuracy of 83.9%. The area under the receiver operating characteristic curves (AUC) for the signatures were 0.895 and 0.881 in the training and testing sets, respectively. CONCLUSION: The prediction model could effectively predict the regrowth of NFPA, which may facilitate the prognostic evaluation and guide early interventions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-019-1915-2) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-20 /pmc/articles/PMC6528212/ /pubmed/31109334 http://dx.doi.org/10.1186/s12967-019-1915-2 Text en © The Author(s) 2019 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 Cheng, Sen Wu, Jiaqi Li, Chuzhong Li, Yangfang Liu, Chunhui Li, Guilin Li, Wuju Hu, Shuofeng Ying, Xiaomin Zhang, Yazhuo Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title | Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title_full | Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title_fullStr | Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title_full_unstemmed | Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title_short | Predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
title_sort | predicting the regrowth of clinically non-functioning pituitary adenoma with a statistical model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6528212/ https://www.ncbi.nlm.nih.gov/pubmed/31109334 http://dx.doi.org/10.1186/s12967-019-1915-2 |
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