Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Cuipeng Qiu; Peng Wang; Bofei Wang; Jianxiang Shi; Xiao Wang; Tiandong Li; Jiejie Qin; Liping Dai; Hua Ye; Jianying Zhang

doi:10.1080/2162402X.2019.1682382

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

Oncoimmunology. 2019 Oct 28;9(1):1682382. doi: 10.1080/2162402X.2019.1682382. eCollection 2020.

Authors

Cuipeng Qiu^{1

2}, Peng Wang^{1

2}, Bofei Wang², Jianxiang Shi^{2

3}, Xiao Wang^{2

3}, Tiandong Li^{1

2}, Jiejie Qin^{1

2}, Liping Dai^{2

3}, Hua Ye^{1

2}, Jianying Zhang^{1

2

3}

Affiliations

¹ Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China.
² College of Public Health, Henan Key Laboratory of Tumor Epidemiology, Zhengzhou University, Zhengzhou, Henan, China.
³ Henan Academy of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan, China.

Abstract

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.

Keywords: Autoantibody; benign breast disease; breast cancer; immunodiagnostic model; tumor-associated antigen.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Antigens, Neoplasm
Autoantibodies
Biomarkers, Tumor
Breast Neoplasms* / diagnosis
Female
Humans
Immunologic Tests

Substances

Antigens, Neoplasm
Autoantibodies
Biomarkers, Tumor

Grants and funding

This study was funded by the National Science and Technology Major Project of China (2018ZX10302205) and Zhengzhou Major Project for Collaborative Innovation (18XTZX12007), the Major Project of Science and Technology in Henan Province (No.161100311400), and the Program of Natural Science Foundation of Henan Province (No. 182300410009).