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Background <p>This study aimed to identify prognostic biomarkers for gastric cancer (GC) by analyzing the methylation status of multiple tumor suppressor genes.</p> Methods <p>Using the Epi-TOP™ methylation detection system, we analyzed 51 genes in 169 matched tumor and adjacent normal tissue samples. Methylation levels were quantified as Percent Methylated Reference (PMR) in tumor (PMR-T) and normal (PMR-N) tissues; the differential methylation (PMR-D) was also calculated.</p> Result <p>Tumor tissues exhibited significantly higher DNA methylation levels than matched normal tissues across 51 tumor suppressor genes (all <i>p</i> &lt; 0.001). Clustering analysis based on PMR-T identified four epigenetic subtypes associated with known molecular classifications (epithelial–mesenchymal transition (EMT) and microsatellite instability–high (MSI-H)) and overall survival (<i>p</i> = 0.030). In contrast, clustering based on PMR-N showed no significant association with molecular subtypes or survival outcomes, suggesting limited prognostic relevance. Two prognostic gene panels were constructed: one PMR-T–based panel (<i>ALX</i>, <i>BMP3</i>, <i>CDKN2A</i>, <i>MINT25</i>, <i>PTGDR</i>) and another PMR-D-based panel (<i>ADCYAP1</i>, <i>SOCS1</i>, <i>SEPTIN9</i>, <i>CDKN2B</i>). Both panels independently predicted overall survival in multivariate Cox regression. The PMR-D panel demonstrated stronger prognostic performance (hazard ratio (HR) = 0.329, <i>p</i> = 0.002), while the PMR-T panel also demonstrated significant prognostic value (HR = 0.512, <i>p</i> = 0.012), highlighting that tumor methylation profiles alone may provide meaningful survival predictions for patients with GC.</p> Conclusion <p>This study demonstrates that tumor-specific DNA methylation changes, particularly when evaluated using multi-gene panels can enhance prognostic stratification in GC. These findings support the potential use of methylation-based biomarkers for personalized management of GC.</p>

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Multi-gene DNA methylation profiles of tumor suppressor genes for prognostic prediction in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Soo Kyung Nam,
  • Juhyeong Park,
  • Yoonjin Kwak,
  • Chinbayar Batochir,
  • Eun-Bi Kim,
  • Seong-Ho Kong,
  • Do Joong Park,
  • Hyuk-Joon Lee,
  • Han-Kwang Yang,
  • Hye Seung Lee

摘要

Background

This study aimed to identify prognostic biomarkers for gastric cancer (GC) by analyzing the methylation status of multiple tumor suppressor genes.

Methods

Using the Epi-TOP™ methylation detection system, we analyzed 51 genes in 169 matched tumor and adjacent normal tissue samples. Methylation levels were quantified as Percent Methylated Reference (PMR) in tumor (PMR-T) and normal (PMR-N) tissues; the differential methylation (PMR-D) was also calculated.

Result

Tumor tissues exhibited significantly higher DNA methylation levels than matched normal tissues across 51 tumor suppressor genes (all p < 0.001). Clustering analysis based on PMR-T identified four epigenetic subtypes associated with known molecular classifications (epithelial–mesenchymal transition (EMT) and microsatellite instability–high (MSI-H)) and overall survival (p = 0.030). In contrast, clustering based on PMR-N showed no significant association with molecular subtypes or survival outcomes, suggesting limited prognostic relevance. Two prognostic gene panels were constructed: one PMR-T–based panel (ALX, BMP3, CDKN2A, MINT25, PTGDR) and another PMR-D-based panel (ADCYAP1, SOCS1, SEPTIN9, CDKN2B). Both panels independently predicted overall survival in multivariate Cox regression. The PMR-D panel demonstrated stronger prognostic performance (hazard ratio (HR) = 0.329, p = 0.002), while the PMR-T panel also demonstrated significant prognostic value (HR = 0.512, p = 0.012), highlighting that tumor methylation profiles alone may provide meaningful survival predictions for patients with GC.

Conclusion

This study demonstrates that tumor-specific DNA methylation changes, particularly when evaluated using multi-gene panels can enhance prognostic stratification in GC. These findings support the potential use of methylation-based biomarkers for personalized management of GC.