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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...

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Detalles Bibliográficos
Autores principales: Qiu, Cuipeng, Wang, Peng, Wang, Bofei, Shi, Jianxiang, Wang, Xiao, Li, Tiandong, Qin, Jiejie, Dai, Liping, Ye, Hua, Zhang, Jianying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2019
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
Descripción
Sumario: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.