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

Abstract

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.