ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
Integrative systems biology analysis reveals diagnostic and prognostic DNA methylation signatures in esophageal cancer.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundEsophageal cancer (EC) is one of the most lethal malignancies worldwide, characterized by insidious early symptoms, late clinical presentation, and poor overall survival (OS). DNA methylation, a key epigenetic modulator, plays a pivotal role in tumorigenesis and represents a promising biomarker for early detection and prognosis due to its high specificity, technical stability, and non-invasive accessibility.
objectiveThis study aimed to systematically identify diagnostic and prognostic DNA methylation signatures in EC by integrating methylome, transcriptome, and clinical data, and to evaluate their potential clinical utility for early diagnosis and prognosis prediction.
methodsDifferentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified using the R limma package. Univariate Cox regression analysis was applied to screen DEGs and DMSs significantly associated with OS (p < 0.05). An interaction network was constructed, and hub methylation sites were prioritized based on degree centrality. Multivariable logistic regression was employed to develop a diagnostic signature. Least absolute shrinkage and selection operator (LASSO), random forest, and multivariable Cox regression were sequentially applied to build a prognostic signature. Predictive performance was evaluated using receiver operating characteristic (ROC) curves.
resultsA total of 7,683 DEGs (adjusted p < 0.05, |log
conclusionWe constructed and validated accurate methylation-based classifiers for early diagnosis and risk stratification of EC patients. These findings provide novel insights and candidate biomarkers for precise management of EC and lay the foundation for developing DNA methylation-based screening and prognostic tools.
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