食管鳞状细胞癌（ESCC）是一种致命的癌症，临床上没有理想的早期诊断生物标志物。本研究的目的是开发和验证一种用户友好的诊断工具，用于早期 ESCC 检测。该研究分为三个阶段：发现、验证和确认，总共包括 1309 名受试者。使用 HuProtTM 人类蛋白质组微阵列分析血清自身抗体，并使用酶联免疫吸附测定 (ELISA) 测量自身抗体水平。采用十二种机器学习算法构建诊断模型，并使用受试者工作特征曲线下面积（AUC）进行评估。该模型应用是通过 R Shiny 提供的图形界面来促进的。在模型中鉴定了 13 种针对 TAA 的自身抗体（CAST、FAM131A、GABPA、HDAC1、HDGFL1、HSF1、ISM2、PTMS、RNF219、SMARCE1、SNAP25、SRPK2 和 ZPR1）。发现阶段。随后的验证和验证阶段确定了 5 种 TAAb（抗 CAST、抗 HDAC1、抗 HSF1、抗 PTMS 和抗 ZPR1），它们在 ESCC 和对照受试者之间表现出显着差异（P<0.05）。支持向量机 (SVM) 模型表现出稳健的性能，训练集中的 AUC 为 0.86（95% CI：0.82-0.89），测试集中的 AUC 为 0.83（95% CI：0.78-0.88）。对于早期 ESCC，SVM 模型在训练集中的 AUC 为 0.83（95% CI：0.79-0.88），在测试集中的 AUC 为 0.83（95% CI：0.77-0.90）。值得注意的是，对于高级别上皮内瘤变观察到了有希望的结果，AUC 为 0.87（95% CI：0.77-0.98）。早期 ESCC 诊断工具的基于网络的实现可在 https://litdong.shinyapps.io/ESCCPred/ 上公开访问。这项研究为早期 ESCC 检测提供了一种有前途且易于使用的诊断预测模型。它有望改善早期检测策略，并对公共健康具有潜在影响。© 2024。作者，获得 Springer Nature Limited 的独家许可。

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.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.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/ .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.© 2024. The Author(s), under exclusive licence to Springer Nature Limited.