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Updated: May 10, 2025
Electrochemiluminescence Assays for Human Islet Autoantibodies
Published on: March 23, 2018
Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a
Yi-Wei Xu1,2, Yu-Hui Peng3,4, Can-Tong Liu3,4
1Department 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 College, Shantou, 515041, China. yiwei512@126.com.
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.
