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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...

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Autores principales: Cheng, Sen, Wu, Jiaqi, Li, Chuzhong, Li, Yangfang, Liu, Chunhui, Li, Guilin, Li, Wuju, Hu, Shuofeng, Ying, Xiaomin, Zhang, Yazhuo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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.
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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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