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Molecular Diagnostics

ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles

British Journal of Cancer (2024)Cite this article

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Abstract

Background

Esophageal squamous cell carcinoma (ESCC) is a deadly cancer with no clinically ideal biomarkers for early diagnosis. The objective of this study was to develop and validate a user-friendly diagnostic tool for early ESCC detection.

Methods

The study encompassed three phases: discovery, verification, and validation, comprising a total of 1309 individuals. Serum autoantibodies were profiled using the HuProtTM human proteome microarray, and autoantibody levels were measured using the enzyme-linked immunosorbent assay (ELISA). Twelve machine learning algorithms were employed to construct diagnostic models, and evaluated using the area under the receiver operating characteristic curve (AUC). The model application was facilitated through R Shiny, providing a graphical interface.

Results

Thirteen autoantibodies targeting TAAs (CAST, FAM131A, GABPA, HDAC1, HDGFL1, HSF1, ISM2, PTMS, RNF219, SMARCE1, SNAP25, SRPK2, and ZPR1) were identified in the discovery phase. Subsequent verification and validation phases identified five TAAbs (anti-CAST, anti-HDAC1, anti-HSF1, anti-PTMS, and anti-ZPR1) that exhibited significant differences between ESCC and control subjects (P < 0.05). The support vector machine (SVM) model demonstrated robust performance, with AUCs of 0.86 (95% CI: 0.82–0.89) in the training set and 0.83 (95% CI: 0.78–0.88) in the test set. For early-stage ESCC, the SVM model achieved AUCs of 0.83 (95% CI: 0.79–0.88) in the training set and 0.83 (95% CI: 0.77–0.90) in the test set. Notably, promising results were observed for high-grade intraepithelial neoplasia, with an AUC of 0.87 (95% CI: 0.77–0.98). The web-based implementation of the early ESCC diagnostic tool is publicly accessible at https://litdong.shinyapps.io/ESCCPred/.

Conclusion

This study provides a promising and easy-to-use diagnostic prediction model for early ESCC detection. It holds promise for improving early detection strategies and has potential implications for public health.

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Fig. 1: The flow diagram of this study.
Fig. 2: Identification of 13 candidate TAAbs based on the HuProtTM protein microarray
Fig. 3: Validation of the TAAb in the training and test set (validation phase).
Fig. 4: Comparative performance evaluation of 12 machine learning models for ESCC diagnosis.
Fig. 5: Diagnostic performance of the SVM model.

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Data availability

All data utilized in this study are accessible from the corresponding authors upon reasonable request. Additionally, the R codes used to develop ESCCPred are openly available at https://github.com/tiandongli/ESCCPred.

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Acknowledgements

The authors extend their sincerest appreciation to the Dr. Shipeng Guo for their invaluable assistance throughout the process of learning R Shiny. Particularly, heartfelt thanks are extended to Prof. Jianying Zhang for his unwavering support and invaluable feedback throughout this research endeavor.

Funding

This work was supported by the Funded Project of International Training of High-level Talents in Henan Province (no grant ID), Zhengzhou Major Project for Collaborative Innovation (18XTZX12007), and Key Research Project of Higher Education in Henan Province (22A330003).

Author information

Author notes

  1. These authors contributed equally: Tiandong Li, Guiying Sun.

Authors and Affiliations

  1. College of Public Health, Zhengzhou University, Zhengzhou, 450001, Henan Provinc, China

    Tiandong Li, Guiying Sun, Hua Ye, Yajing Shen, Yifan Cheng, Yuanlin Zou, Zhaoyang Fang & Peng Wang

  2. Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou, 450052, Henan Province, China

    Tiandong Li, Guiying Sun, Hua Ye, Yajing Shen, Yifan Cheng, Yuanlin Zou, Zhaoyang Fang, Jianxiang Shi, Keyan Wang, Liping Dai & Peng Wang

  3. Henan Children’s Hospital, Children’s Hospital Affiliated of Zhengzhou University, Zhengzhou, 450018, Henan Province, China

    Guiying Sun

  4. The Institution for Chronic and Noncommunicable Disease Control and Prevention, Zhengzhou Center for Disease Control and Prevention, Zhengzhou, 450052, Henan Provinc, China

    Caijuan Song

  5. Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, 450052, Henan, China

    Jianxiang Shi, Keyan Wang & Liping Dai

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Contributions

PW designed the study, TL and GS conducted the experiments and analyzed data, TL wrote the manuscript and GS, HY, CS, YC, YZ, YS, ZF, JS, KW, LD revised the full manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Peng Wang.

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The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of Zhengzhou University (ZZURIB2019001). All clinical sample patients were informed of the purpose of the study and signed the consent form.

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The manuscript was read and approved for publication by all participants.

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Li, T., Sun, G., Ye, H. et al. ESCCPred: a machine learning model for diagnostic prediction of early esophageal squamous cell carcinoma using autoantibody profiles. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02781-w

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