ArticleOncoimmunology
Using a panel of multiple tumor-associated antigens to enhance autoantibody detection for immunodiagnosis of gastric cancer.
Article in Oncoimmunology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Antibodies as biomarkers for cancer risk: a systematic review.Clinical and experimental immunology · 2022Pooled it
- Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review.Advances in therapy · 2021Pooled it
- The B-cell-autoantibody axis in lung cancer immunity.Theranostics · 2026Review
- A nomogram based on autoantibodies for noninvasive detection of AFP-negative hepatocellular carcinoma: a multicenter study.British journal of cancer · 2025Article
- Establishment and validation of a nomogram model for early diagnosis of gastric cancer: a large-scale cohort study.Frontiers in oncology · 2024Article
- ZPR1 is an immunodiagnostic biomarker and promotes tumor progression in esophageal squamous cell carcinoma.Cancer science · 2024Article
- Risk Prediction Models for Gastric Cancer: A Scoping Review.Journal of multidisciplinary healthcare · 2024Article
- Global progress and future prospects of early gastric cancer screening.Journal of Cancer · 2024Review
- B cell profiles, antibody repertoire and reactivity reveal dysregulated responses with autoimmune features in melanoma.Nature communications · 2023Article
- Serum anti-SPP1 autoantibody as a potential novel biomarker in detection of esophageal squamous cell carcinoma.BMC cancer · 2022Article
- Temporal reproducibility of IgG and IgM autoantibodies in serum from healthy women.Scientific reports · 2022Article
- A novel immunodiagnosis panel for hepatocellular carcinoma based on bioinformatics and the autoantibody-antigen system.Cancer science · 2022Article
- Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma.Frontiers in genetics · 2022Article
- Anti-POSTN and Anti-TIMP1 Autoantibodies as Diagnostic Markers in Esophageal Squamous Cell Carcinoma.Frontiers in genetics · 2022Article
- Beneficial autoimmunity improves cancer prognosis.Nature reviews. Clinical oncology · 2021Review
- Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer.Cancer science · 2021Article
- Nucleophosmin 1 overexpression correlates withEuropean journal of nuclear medicine and molecular imaging · 2021Article
- Review
- Using protein microarray to identify and evaluate autoantibodies to tumor-associated antigens in ovarian cancer.Cancer science · 2021Article
- Serum Anti-14-3-3 Zeta Autoantibody as a Biomarker for Predicting Hepatocarcinogenesis.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoantibodies against tumor-associated antigens (TAAs) are attractive non-invasive biomarkers for detection of cancer due to their inherently stable in serum. Serum autoantibodies against 9 TAAs from gastric cancer (GC) patients and healthy controls were measured by enzyme-linked immunosorbent assay (ELISA). A logistic regression model predicting the risk of being diagnosed with GC in the training cohort (n = 558) was generated and then validated in an independent cohort (n = 372). Area under the receiver operating characteristic curve (AUC) was used to assess the diagnostic performance. Finally, an optimal prediction model with 6 TAAs (p62, c-Myc, NPM1, 14-3-3ξ, MDM2 and p16) showed a great diagnostic performance of GC with AUC of 0.841 in the training cohort and 0.856 in the validation cohort. The proportion of subjects being correctly defined were 78.49% in the training cohort and 81.99% in the validation cohort. This prediction model could also differentiate early-stage (stage I-II) GC patients from healthy controls with sensitivity/specificity of 76.60%/72.34% and 80.56%/79.17% in the training and validation cohort, respectively, and the overall sensitivity/specificity for early-stage GC were 78.92%/74.70% when being combined with two cohorts. This prediction model presented no significant difference for the diagnostic accuracy between early-stage and late-stage (stage III - IV) GC patients. The model with 6 TAAs showed a high diagnostic performance for GC detection, particularly for early-stage GC. This study further supported the hypothesis that a customized array of multiple TAAs was able to enhance autoantibody detection in the immunodiagnosis of GC.
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