4.6 Article

Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information

Journal

GENES
Volume 13, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/genes13101913

Keywords

pancreatic adenocarcinoma; DNA methylation; prognosis; consistency clustering; tumor classification

Funding

  1. Haiyan Foundation of Harbin Medical University Cancer Hospital [JJMS2021-18]
  2. Beijing Medical Award Foundation [YXJL-2020-1191-0237]

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This study identified differential methylated genes and cancer driver genes between two subtypes of Pancreatic adenocarcinoma (PAAD) through analysis of DNA methylation data and transcriptome data, and constructed a prognostic prediction model based on specific methylated genes. The findings shed new light on the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.
Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.

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