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Background <p>During perioperative care for non-small cell lung cancer (NSCLC) patients, clinical outcomes vary significantly. There is a critical need for more dependable biomarkers to identify high-risk individuals in the perioperative phase. This is essential for enhancing postoperative interventions and positively influencing clinical results.</p> Method <p>We collected a tissue DNA methylation cohort of 73 stage I–III surgically treated patients as the discovery set for model development. The model was established using recurrence-free survival (RFS) as the primary endpoint. Subsequently, its prognostic value was validated in an independent cohort of 30 stage I–III surgical patients, and further confirmed across different patient subgroups.</p> Results <p>We developed an Early to Mid-term NSCLC Recurrence LASSO score (EMRL) predictive model based on five differentially methylated regions (DMRs). The EMRL model was significantly associated with RFS in stage I-III surgically treated patients (RFS: log-rank <i>P</i> = 0.00032) and was confirmed as an independent prognostic factor in multivariate Cox regression analysis (HR = 0.35, 95% confidence interval 0.20–0.61, <i>P</i> &lt; 0.001). Notably, EMRL not only identified high-risk patients within the same TNM stage but also demonstrated strong predictive performance in patient subgroups harboring EGFR-TKI-sensitive mutations and those with positive PD-L1 expression.</p> Conclusion <p>In this study, we developed a postoperative recurrence prediction model based on preoperative tissue methylation characteristics to identify individuals in I-III stage NSCLC patients following surgical resection who may have a higher risk of recurrence. This offers opportunities for early personalized treatment and follow-up strategy.</p>

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Identification and validation of a DNA methylation-block prognostic model in non-small cell lung cancer patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Hang Li,
  • Yi Lu,
  • Haiqing Chen,
  • Tong Li,
  • Fangqiu Fu,
  • Jing Wang,
  • Bing Li,
  • Hong Hu

摘要

Background

During perioperative care for non-small cell lung cancer (NSCLC) patients, clinical outcomes vary significantly. There is a critical need for more dependable biomarkers to identify high-risk individuals in the perioperative phase. This is essential for enhancing postoperative interventions and positively influencing clinical results.

Method

We collected a tissue DNA methylation cohort of 73 stage I–III surgically treated patients as the discovery set for model development. The model was established using recurrence-free survival (RFS) as the primary endpoint. Subsequently, its prognostic value was validated in an independent cohort of 30 stage I–III surgical patients, and further confirmed across different patient subgroups.

Results

We developed an Early to Mid-term NSCLC Recurrence LASSO score (EMRL) predictive model based on five differentially methylated regions (DMRs). The EMRL model was significantly associated with RFS in stage I-III surgically treated patients (RFS: log-rank P = 0.00032) and was confirmed as an independent prognostic factor in multivariate Cox regression analysis (HR = 0.35, 95% confidence interval 0.20–0.61, P < 0.001). Notably, EMRL not only identified high-risk patients within the same TNM stage but also demonstrated strong predictive performance in patient subgroups harboring EGFR-TKI-sensitive mutations and those with positive PD-L1 expression.

Conclusion

In this study, we developed a postoperative recurrence prediction model based on preoperative tissue methylation characteristics to identify individuals in I-III stage NSCLC patients following surgical resection who may have a higher risk of recurrence. This offers opportunities for early personalized treatment and follow-up strategy.