Development and validation of a tissue-based DNA methylation risk-score model to predict the prognosis of surgically resected pancreatic cancer patients.

BACKGROUND

Pancreatic cancer (PC) is a highly malignant tumor associated with low survival rates. It is challenging to predict the survival of surgically resected patients with PC. A prognostic staging tool could be beneficial to guide treatments and also aid post-treatment surveillance. This study aimed to identify tissue-based DNA methylation risk-score model to predict the prognosis of surgically resected pancreatic cancer patients.

METHODS

We performed a monocentric, retrospective study that included 50 patients with stage I-II PC from The First Affiliated Hospital of Soochow University (SU cohort). Both tumor and adjacent normal tissues were obtained from each patient and subjected to capture-based targeted methylation profiling.

RESULTS

In total, 1,162 DNA methylation blocks (DMBs) were differentially methylated in tumor tissues compared with adjacent long-distance tissues (P<0.05). Least Absolute Shrinkage and Selection Operator (LASSO) and stepwise regression analyses revealed a significant correlation between the methylation signature (risk score) and overall survival (OS). Patients in the high-risk group showed significantly poorer OS than those in the low-risk group in the survival analysis [P≤0.001; area under curve (AUC) at 1 year, 0.789; AUC at 2 years, 0.852]. The risk score was also validated using clinical and methylation data of 166 PC patients from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PDAC) dataset. Patients in the high-risk group showed significantly poorer OS than those in the low-risk group (P=0.004; AUC at 1 years, 0.677; AUC at 3 years, 0.611). When clinical parameters were considered, the risk score was the only independent prognostic parameter (P<0.001) in the Cox regression analysis. Furthermore, low-risk patients had higher levels of immune infiltration, anti-tumor immune activation, and increased sensitivity to gemcitabine and paclitaxel. In contrast, high-risk patients had lower mutation rates and benefited more from cisplatin.

CONCLUSIONS

In our study, we constructed and validated a tissue-based DNA methylation risk-score model to predict prognosis and identify PC patients with a high mortality risk at the time of surgery. This model might provide a tissue-based prognostic assessment tool for clinicians to aid their treatment decision-making.