Abstract
Introduction: Esophageal squamous cell carcinoma (ESCC) is the most common type of esophageal cancer worldwide. The gold standard for its diagnosis requires esophagogastroduodenoscopy, but this is not feasible as a screening modality due to high cost and risk. Therefore, we sought to develop a diagnostic strategy using ESCC-specific methylated DNA biomarkers on esophageal cells obtained via minimally invasive method. Methods: Using genome-wide methylation data and bioinformatics, we identified candidate ESCC-specific methylated DNA biomarkers, which were initially tested on 48 ESCC-matched normal tissue pairs. These candidate biomarkers were further evaluated in a cross-sectional case-control study of 63 patients from whom esophageal cytology samples were collected using a minimally invasive sponge device. Finally, a multi-biomarker model for ESCC diagnosis was created using a machine learning pipeline with the random subspace method. Results: Five of the 6 biomarkers (cg20655070, SLC35F1, TAC1, ZNF132, and ZNF542) exhibited significantly higher methylation levels in tumor DNA vs matched control tissue DNAs (P < 0.05). When tested on sponge-derived esophageal cytology DNA, these 5 markers were all significantly hypermethylated in ESCC vs non-ESCC control DNAs (P < 0.01), and all 5 distinguished ESCCs from controls with areas under receiver operating characteristics curve (AUCs) > 0.76 (P < 0.01). Finally, a discriminatory 5-marker-plus-age diagnostic panel was then developed, which demonstrated outstanding biomarker performance (sensitivity = 0.91, specificity = 0.96, and AUC = 0.90). Conclusion: This highly discriminatory biomarker panel is a promising strategy for ESCC diagnosis using low-cost, minimally invasive sampling techniques and merits further study in prospective cancer screening trials.
Published Version
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