bims-dinmec Biomed News
on DNA methylation in cancer
Issue of 2026–06–07
two papers selected by
Lorena Ancona, Humanitas Research
  1. Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.
  2. Integrative systems biology analysis reveals diagnostic and prognostic DNA methylation signatures in esophageal cancer.
  1. NPJ Precis Oncol. 2026 Jun 02.
    Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.
    Qi Lin, Yifan Liu, Hanlin Lu, Zhixiong Wang, Wei Tang, Yulong He, Juyong Zhang, Guanghua Li.
      Development of sensitive biomarkers is required to achieve early detection and tumor burden monitoring in gastric cancer (GC). We performed genome-wide methylation sequencing on 78 tissue and 241 plasma samples from 171 GC patients and 114 healthy controls from two independent clinical centers. Differentially methylated regions (DMRs) were screened using paired GC and normal tissues, and refined through cfDNA profiles with LASSO regression to construct a cfDNA-based biomarker, the GCML-score. The GCML-score, consisting of 13 DMRs, demonstrated excellent diagnostic performance (AUC: 0.95/0.99/0.95 overall and 0.96/0.99/0.82 in early GC for training/internal validation/external validation cohorts). In 12 patients receiving neoadjuvant chemotherapy, dynamic changes in GCML-score were consistent with radiological tumor burden, highlighting its monitoring potential. The GCML-score, derived from genome-wide cfDNA methylation profiling, provides a robust tool for early GC detection and real-time tumor burden monitoring, facilitating improved prognosis and personalized therapeutic strategies.
    DOI:  https://doi.org/10.1038/s41698-026-01525-8
  2. Clin Transl Oncol. 2026 Jun 04.
    Integrative systems biology analysis reveals diagnostic and prognostic DNA methylation signatures in esophageal cancer.
    Yifan Zong, Kaige Li, Wenhui Sun, Minghao Yao, Liucun Zhu, Wenna Guo.
       BACKGROUND: Esophageal 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.
    OBJECTIVE: This 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.
    METHODS: Differentially 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.
    RESULTS: A total of 7,683 DEGs (adjusted p < 0.05, |log2fold-change (FC)|> 1) and 29,122 DMSs (adjusted p < 0.001) were identified. Among these, 891 DMSs and 270 DEGs were significantly associated with OS (p < 0.05), and the top 30 hub DNA methylation sites were prioritized. A 2-CpG diagnostic model (cg03850256 and cg11394785) achieved an area under the curve (AUC) of 0.994, demonstrating excellent diagnostic accuracy. A 4-CpG prognostic signature (cg05768047, cg22724943, cg08363794, and cg10405610) showed robust predictive performance for survival outcomes (AUC: 0.821).
    CONCLUSION: We 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.
    Keywords:  Biomarker; DNA methylation; Diagnosis; Esophageal cancer; Prognosis; Systems biology
    DOI:  https://doi.org/10.1007/s12094-026-04411-8
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