Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

Chao Song; Ganggang Wang; Mengmeng Liu; Zijin Xu; Xin Liang; Kai Ding; Yu Chen; Wenquan Wang; Wenhui Lou; Liang Liu

doi:10.1016/j.heliyon.2024.e29914

Identification of methylation driver genes for predicting the prognosis of pancreatic cancer patients based on whole-genome DNA methylation sequencing technology

Heliyon. 2024 Apr 22;10(9):e29914. doi: 10.1016/j.heliyon.2024.e29914. eCollection 2024 May 15.

Authors

Chao Song^{1

2

3}, Ganggang Wang⁴, Mengmeng Liu⁵, Zijin Xu³, Xin Liang⁶, Kai Ding⁶, Yu Chen⁶, Wenquan Wang², Wenhui Lou², Liang Liu^{1

2}

Affiliations

¹ Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200000, China.
² Department of Pancreatic Surgery, Affiliated Zhongshan Hospital of Fudan University, Shanghai, 200000, China.
³ Department of General Surgery, Affiliated Zhongshan Hospital of Fudan University, Qingpu Branch, Shanghai, 200000, China.
⁴ Department of Hepatobiliary Surgery, Pudong Hospital, Fudan University, Shanghai, 200000, China.
⁵ Department of Gastroenterology, Affiliated Zhongshan Hospital of Fudan University, Qingpu Branch, Shanghai, 200000, China.
⁶ CAS Key Laboratory of Nutrition, University of Chinese Academy of Sciences, Shanghai, 200000, China.

Abstract

This study was based on the use of whole-genome DNA methylation sequencing technology to identify DNA methylation biomarkers in tumor tissue that can predict the prognosis of patients with pancreatic cancer (PCa). TCGA database was used to download PCa-related DNA methylation and transcriptome atlas data. Methylation driver genes (MDGs) were obtained using the MethylMix package. Candidate genes in the MDGs were screened for prognostic relevance to PCa patients by univariate Cox analysis, and a prognostic risk score model was constructed based on the key MDGs. ROC curve analysis was performed to assess the accuracy of the prognostic risk score model. The effects of PIK3C2B knockdown on malignant phenotypes of PCa cells were investigated in vitro. A total of 2737 differentially expressed genes were identified, with 649 upregulated and 2088 downregulated, using 178 PCa samples and 171 normal samples. MethylMix was employed to identify 71 methylation-driven genes (47 hypermethylated and 24 hypomethylated) from 185 TCGA PCa samples. Cox regression analyses identified eight key MDGs (LEF1, ZIC3, VAV3, TBC1D4, FABP4, MAP3K5, PIK3C2B, IGF1R) associated with prognosis in PCa. Seven of them were hypermethylated, while PIK3C2B was hypomethylated. A prognostic risk prediction model was constructed based on the eight key MDGs, which was found to accurately predict the prognosis of PCa patients. In addition, the malignant phenotypes of PANC-1 cells were decreased after the knockdown of PIK3C2B. Therefore, the prognostic risk prediction model based on the eight key MDGs could accurately predict the prognosis of PCa patients.

Keywords: Methylation driver genes; Pancreatic cancer; Prognostic markers; Prognostic risk prediction model; TCGA database; Whole-genome DNA methylation.