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Establishment and validation of an immunodiagnostic model for prediction of breast cancer
Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguis...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959442/ https://www.ncbi.nlm.nih.gov/pubmed/32002291 http://dx.doi.org/10.1080/2162402X.2019.1682382 |
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author | Qiu, Cuipeng Wang, Peng Wang, Bofei Shi, Jianxiang Wang, Xiao Li, Tiandong Qin, Jiejie Dai, Liping Ye, Hua Zhang, Jianying |
author_facet | Qiu, Cuipeng Wang, Peng Wang, Bofei Shi, Jianxiang Wang, Xiao Li, Tiandong Qin, Jiejie Dai, Liping Ye, Hua Zhang, Jianying |
author_sort | Qiu, Cuipeng |
collection | PubMed |
description | Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919–0.967) in training cohort and 0.916 (95% CI, 0.886–0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848–0.914) and 0.849 (95% CI, 0.803–0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer. |
format | Online Article Text |
id | pubmed-6959442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-69594422020-01-30 Establishment and validation of an immunodiagnostic model for prediction of breast cancer Qiu, Cuipeng Wang, Peng Wang, Bofei Shi, Jianxiang Wang, Xiao Li, Tiandong Qin, Jiejie Dai, Liping Ye, Hua Zhang, Jianying Oncoimmunology Original Research Serum autoantibodies that react with tumor-associated antigens (TAAs) can be used as potential biomarkers for diagnosis of cancer. This study aims to evaluate the immunodiagnostic value of 11 anti-TAAs autoantibodies for detection of breast cancer (BC) and establish a diagnostic model for distinguishing BC from normal human controls (NHC) and benign breast diseases (BBD). Sera from 10 BC patients and 10 NHC were used to detect 11 anti-TAAs autoantibodies by western blotting. The 11 anti-TAAs autoantibodies were further assessed in 983 sera by relative quantitative enzyme-linked immunosorbent assay (ELISA). Binary logistic regression and Fisher linear discriminant analysis were conducted to establish a prediction model by using 184 BC and 184 NHC (training cohort, n = 568) and validated by leave-one-out cross-validation. Logistic regression model was selected to establish the prediction model. Results were validated using an independent validation cohort (n = 415). The five anti-TAAs (p53, cyclinB1, p16, p62, 14-3-3ξ) autoantibodies were selected to construct the model with the area under the curve (AUC) of 0.943 (95% CI, 0.919–0.967) in training cohort and 0.916 (95% CI, 0.886–0.947) in the validation cohort. In the identification of BC and BBD, AUCs were 0.881 (95% CI, 0.848–0.914) and 0.849 (95% CI, 0.803–0.894) in training and validation cohort, respectively. In summary, our study indicates that the immunodiagnostic model can distinguish BC from NHC and BC from BBD and this model may have a potential application in immunodiagnosis of breast cancer. Taylor & Francis 2019-10-28 /pmc/articles/PMC6959442/ /pubmed/32002291 http://dx.doi.org/10.1080/2162402X.2019.1682382 Text en © 2019 The Author(s). Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Qiu, Cuipeng Wang, Peng Wang, Bofei Shi, Jianxiang Wang, Xiao Li, Tiandong Qin, Jiejie Dai, Liping Ye, Hua Zhang, Jianying Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title | Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title_full | Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title_fullStr | Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title_full_unstemmed | Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title_short | Establishment and validation of an immunodiagnostic model for prediction of breast cancer |
title_sort | establishment and validation of an immunodiagnostic model for prediction of breast cancer |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6959442/ https://www.ncbi.nlm.nih.gov/pubmed/32002291 http://dx.doi.org/10.1080/2162402X.2019.1682382 |
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