What question did this study set out to answer?

The aim is to identify tumor-associated autoantibodies and develop a diagnostic model for early detection of esophageal squamous cell carcinoma.

February 13, 2026Open Access

Multi-dimensional integration of gene expression, protein evidence, and serum autoantibodies for diagnostic modeling in esophageal squamous cell carcinoma

YZYuanlin ZouMicron (United States)HWHan WangZhengzhou University CSCaijuan SongZhengzhou University

Key Points

The aim is to identify tumor-associated autoantibodies and develop a diagnostic model for early detection of esophageal squamous cell carcinoma.
Integrated transcriptomic screening of public datasets and literature evidence
Serological evaluation of autoantibodies using ELISA in 940 samples
Assessment of eight machine learning algorithms to develop a diagnostic model
Validation of the model in an independent test set.
Identified 26 differentially expressed genes with ten supporting protein evidence as candidate tumor-associated antigens
Seven autoantibodies significantly elevated in esophageal cancer cases compared to normal controls
SVM model showed the best diagnostic performance with AUCs of 0.826 in training and 0.741 in internal test set
The model was validated achieving an AUC of 0.779 in independent testing.

Abstract

Introduction Esophageal squamous cell carcinoma (ESCC) accounts for most esophageal cancer cases. This study implemented a multi-dimensional integrative approach to identify tumor-associated autoantibodies (TAAbs) and develop a diagnostic model for the early detection of ESCC. Methods The study comprised four phases: discovery, verification, modeling, and evaluation. Transcriptomic screening of public datasets and protein-level evidence from literature were integrated to identify candidate tumor-associated antigens (TAAs), followed by serological evaluation using enzyme-linked immunosorbent assay (ELISA) in 940 samples. Eight machine learning algorithms were assessed to develop the optimal diagnostic model. Results In the discovery phase, transcriptomic analysis identified 26 differentially expressed genes in ESCC, of which ten genes encoding proteins with literature-supported evidence were selected as candidate TAAs for serological testing. Seven TAAbs were significantly elevated in ESCC cases compared with normal controls in the verification phase. In the modeling phase, six TAAbs (anti-CEP55, anti-CKS1B, anti-ECT2, anti-KIF2C, anti-SURV, and anti-TPX2) remained elevated in ESCC cases compared with both benign esophageal disease and normal controls. The support vector machine (SVM) model demonstrated the best diagnostic performance, achieving AUCs of 0. 826 (95% CI: 0. 776–0. 876) in the training set and 0. 741 (95% CI: 0. 651–0. 832) in the internal test set. In the evaluation phase, the SVM model was validated in an independent temporal test set (AUC 0. 779, 95% CI 0. 717–0. 842). The web-based diagnostic tool is accessible at https: //linzou. shinyapps. io/ESCCSVMModel/. Conclusion This multi-dimensional approach linking transcriptomic evidence, protein-level validation, and immunodiagnostic markers facilitated the development of a diagnostic model, which may hold promise for early detection of ESCC.

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