Combining non-invasive liquid biopsy and a methylation analysis to assess surgical risk for early esophageal cancer

While the widespread use of endoscopic submucosal dissection (ESD) has significantly reduced the incidence of early esophageal cancer (ESCA), the limited ability of ESD to strip deep infiltrating esophageal lesions

This study established a novel approach that integrates non-invasive testing with a methylation analysis to assess the surgical risk of early ESCA patients. The significance of changes in methylation sites in relation to lesion status should not be underestimated, as they have the potential to offer vital insights for proactive risk assessments before surgery.

In this study, we conducted an analysis of gene expression differences between stage I esophageal squamous carcinoma samples and healthy tissue samples using data from The Cancer Genome Atlas (TCGA) database. We also identified the genes associated with progression-free survival (PFS) in esophageal squamous carcinoma. Integrating the framework of the methylation analysis, we explored the methylated sites of these distinct genes. To refine this process, we used the Shiny Methylation Analysis Resource Tool (SMART) to conduct a comprehensive analysis of these sites. We then confirmed the stability of the methylation sites in different lesion conditions using methylation-specific quantitative polymerase chain reaction (MS-qPCR) with paraffin tissue samples collected after ESD.

We analyzed RNA-sequencing data from 42 early stage ESCA patients and 17 controls, identifying 1,263 up-regulated and 460 down-regulated genes. Functional analyses revealed involvement in key pathways such as cell cycle regulation and immune responses. Furthermore, we identified 38 differentially expressed genes associated with PFS. Using SMART analysis, we found 217 hyper-methylated regions in 38 genes, suggesting potential early markers for ESCA. Validation experiments confirmed the reliability of 29 hyper-methylated regions in FFPE tissue samples and 6 regions in cfDNA. A LunaCAM model showed high accuracy [area under the curve (AUC) =0.89] in discriminating early ESCA. Integrated assessment of six highly methylated regions significantly improved predictive performance, with 90.56% sensitivity, highlighting the importance of combinatorial biomarker evaluation for early cancer detection. Conclusions: This study established a novel approach that integrates non-invasive testing with a methylation analysis to assess the surgical risk of early ESCA patients. The significance of changes in methylation sites in relation to lesion status should not be underestimated, as they have the potential to offer vital insights for proactive risk assessments before surgery.