ArticleBMC medicine2025
Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.
Article in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Plasma autoantibody profiling via PhIP-seq and machine learning for diagnosis of esophageal squamous cell carcinoma.Nature communications · 2026Observational
- Machine learning applications in the detection and treatment of esophageal cancer.Discover oncology · 2026Review
- Review
- Review on the significance and clinical application of anti-p53 autoantibody detection.Frontiers in immunology · 2026Review
- Spatiotemporal Analysis of Molecular Continuity in Pathogenesis of Squamous Cell Carcinoma: From Normal Epithelium to Precancerous Lesions and Carcinoma.Cancer communications (London, England) · 2026Review
- Tumor antigen-associated autoantibodies: generation mechanisms, roles in tumorigenesis and progression, and clinical application prospects.Frontiers in immunology · 2026Review
- Development and Validation of an Interpretable Machine Learning Model Based on Peripheral Blood Biomarkers for Esophageal Cancer Risk Prediction.International journal of general medicine · 2026Article
- Identification and Experimental Validation of Key Biomarkers for Rheumatoid Arthritis Based on Bioinformatics Analysis and Machine Learning.Journal of inflammation research · 2026Article
- Multi-dimensional integration of gene expression, protein evidence, and serum autoantibodies for diagnostic modeling in esophageal squamous cell carcinoma.Frontiers in immunology · 2026Article
- Computational Approach Improves Diagnostic Performance Based on Tumor-Associated Autoantibodies in Esophageal Squamous Cell Carcinoma.Computational and structural biotechnology journal · 2026Article
- Machine learning-based prediction of preeclampsia using first-trimester inflammatory markers and red blood cell indices.BMC pregnancy and childbirth · 2025Article
- Genetic Associations of Clonal Hematopoiesis With Cardioembolic Stroke: Insights From Genome-Wide Mendelian Randomization, Bulk RNA, Single-Cell RNA Sequencing.CNS neuroscience & therapeutics · 2025Article
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
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11 authors.
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Abstract
backgroundAutoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high diagnostic accuracy for clinical and preclinical esophageal squamous cell carcinoma (ESCC) using machine learning (ML) algorithms.
methodsWe identified potential autoantibodies against tumor-associated antigens with serological proteome analysis. Serum autoantibody levels were measured by ELISA. Using a training set (n = 531), 102 models based on ML algorithms were constructed, and Partial Least Squares Generalized Linear Models (plsRglm) was selected out using receiver operating characteristics (ROC), Kolmogorov-Smirnov (K-S) test, and Population Stability Index (PSI), and further validated through an internal validation set (n = 413), external validation set 1 (n = 371), and external validation set 2 (n = 202). Then, we validated the ability of plsRglm model in predicting preclinical ESCC by a nested case-control study (24 preclinical ESCCs and 112 matched controls) within a population-based prospective cohort study.
resultsROC analysis, K-S test, and PSI showed that plsRglm model based on four autoantibodies (ALDOA, ENO1, p53, and NY-ESO-1) exhibited the better diagnostic performance and robustness, which provided a high diagnostic accuracy in diagnosing ESCC with the respective AUCs (sensitivities and specificities) of 0.860 (68.8% and 90.4%) in the training set, 0.826 (65.3% and 89.1%) in the internal validation set, and 0.851 (69.2% and 87.3%) in the external validation set 1. For early-stage ESCC, this signature also maintained diagnostic performance [0.817 (62.3% and 90.4%) in the training set; 0.842 (62.5% and 89.1%) in the internal validation set; 0.854 (63.2% and 87.3%) in the external validation set 1; and 0.850 (67.3% and 90.1%) in the external validation set 2]. In the nested case-control study, this plsRglm model could detect the presence of preclinical ESCC with the AUC of 0.723, sensitivity of 54.2%, and specificity of 86.6%.
conclusionsOur findings indicated that the plsRglm model based on four autoantibodies might help identify preclinical and early-stage ESCC.
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