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
Abstract Background Autoantibodies 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 preclin...
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2025-04-01
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| Online Access: | https://doi.org/10.1186/s12916-025-04066-2 |
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| author | Yi-Wei Xu Yu-Hui Peng Can-Tong Liu Hao Chen Ling-Yu Chu Hai-Lu Chen Zhi-Yong Wu Wen-Qiang Wei Li-Yan Xu Fang-Cai Wu En-Min Li |
| author_facet | Yi-Wei Xu Yu-Hui Peng Can-Tong Liu Hao Chen Ling-Yu Chu Hai-Lu Chen Zhi-Yong Wu Wen-Qiang Wei Li-Yan Xu Fang-Cai Wu En-Min Li |
| author_sort | Yi-Wei Xu |
| collection | DOAJ |
| description | Abstract Background Autoantibodies 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. Methods We 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. Results ROC 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%. Conclusions Our findings indicated that the plsRglm model based on four autoantibodies might help identify preclinical and early-stage ESCC. |
| format | Article |
| id | doaj-art-48ff57b1c9b741379c0fcafcb059fcca |
| institution | OA Journals |
| issn | 1741-7015 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | BMC Medicine |
| spelling | doaj-art-48ff57b1c9b741379c0fcafcb059fcca2025-08-20T02:20:05ZengBMCBMC Medicine1741-70152025-04-0123111510.1186/s12916-025-04066-2Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case–control studyYi-Wei Xu0Yu-Hui Peng1Can-Tong Liu2Hao Chen3Ling-Yu Chu4Hai-Lu Chen5Zhi-Yong Wu6Wen-Qiang Wei7Li-Yan Xu8Fang-Cai Wu9En-Min Li10Department of Clinical Laboratory Medicine, Esophageal Cancer Prevention and Control Research Center, Chaoshan Branch of State Key Laboratory for Esophageal Cancer Prevention and Treatment, Cancer Hospital of Shantou University Medical CollegeDepartment of Clinical Laboratory Medicine, Esophageal Cancer Prevention and Control Research Center, Chaoshan Branch of State Key Laboratory for Esophageal Cancer Prevention and Treatment, Cancer Hospital of Shantou University Medical CollegeDepartment of Clinical Laboratory Medicine, Esophageal Cancer Prevention and Control Research Center, Chaoshan Branch of State Key Laboratory for Esophageal Cancer Prevention and Treatment, Cancer Hospital of Shantou University Medical CollegeState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer CenterDepartment of Clinical Laboratory Medicine, Esophageal Cancer Prevention and Control Research Center, Chaoshan Branch of State Key Laboratory for Esophageal Cancer Prevention and Treatment, Cancer Hospital of Shantou University Medical CollegeDepartment of Surgical Oncology, Shantou Central HospitalDepartment of Surgical Oncology, Shantou Central HospitalDepartment of Cancer Epidemiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeInstitute of Oncological Pathology, Shantou University Medical CollegeDepartment of Radiation Oncology, Esophageal Cancer Prevention and Control Research Center, Cancer Hospital of Shantou University Medical CollegeEsophageal Cancer Prevention and Control Research Center, Chaoshan Branch of State Key Laboratory for Esophageal Cancer Prevention and Treatment, Cancer Hospital of Shantou University Medical CollegeAbstract Background Autoantibodies 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. Methods We 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. Results ROC 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%. Conclusions Our findings indicated that the plsRglm model based on four autoantibodies might help identify preclinical and early-stage ESCC.https://doi.org/10.1186/s12916-025-04066-2AutoantibodyEsophageal cancerEarly detectionMachine learning |
| spellingShingle | Yi-Wei Xu Yu-Hui Peng Can-Tong Liu Hao Chen Ling-Yu Chu Hai-Lu Chen Zhi-Yong Wu Wen-Qiang Wei Li-Yan Xu Fang-Cai Wu En-Min Li 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 BMC Medicine Autoantibody Esophageal cancer Early detection Machine learning |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Autoantibody Esophageal cancer Early detection Machine learning |
| url | https://doi.org/10.1186/s12916-025-04066-2 |
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