ArticleOncoimmunology2020
Establishment and validation of an immunodiagnostic model for prediction of breast cancer.
Article in Oncoimmunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of prediction models for risk of breast cancer.BMC cancer · 2025Pooled it
- Can serum autoantibodies be a potential early detection biomarker for breast cancer in women? A diagnostic test accuracy review and meta-analysis.Systematic reviews · 2022Pooled it
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.The breast journal · 2026Article
- Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.Journal of inflammation research · 2026Article
- From cold to hot tumors: feasibility of applying therapeutic insights to TNBC.Discover oncology · 2025Review
- Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers.BMC cancer · 2025Article
- Development and Validation of Chinese Version of Dental Pain Screening Questionnaire.International dental journal · 2025Article
- Article
- Autoantibodies to PAX5, PTCH1, and GNA11 as Serological Biomarkers in the Detection of Hepatocellular Carcinoma in Hispanic Americans.International journal of molecular sciences · 2023Article
- Novel chimeric antigen receptor T cell-based immunotherapy: a perspective for triple-negative breast cancer.Frontiers in cell and developmental biology · 2023Review
- A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system.Cancer science · 2022Article
- High-throughput and multi-phases identification of autoantibodies in diagnosing early-stage breast cancer and subtypes.Cancer science · 2022Article
- Autoantibodies as biomarkers for breast cancer diagnosis and prognosis.Frontiers in immunology · 2022Review
- Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma.Frontiers in genetics · 2022Article
- Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer.Cancer science · 2021Article
- Olfactory Detection of Toluene by Detection Rats for Potential Screening of Lung Cancer.Sensors (Basel, Switzerland) · 2021Article
- Review
- A Diagnostic Model With IgM Autoantibodies and Carcinoembryonic Antigen for Early Detection of Lung Adenocarcinoma.Frontiers in immunology · 2021Article
- Autoantibodies against p53, MMP-7, and Hsp70 as Potential Biomarkers for Detection of Nonmelanoma Skin Cancers.Disease markers · 2021Article
- Identification of Novel Autoantibodies Based on the Human Proteomic Chips and Evaluation of Their Performance in the Detection of Gastric Cancer.Frontiers in oncology · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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