Song Chao, Wang Ganggang, Liu Mengmeng, Xu Zijin, Liang Xin, Ding Kai, Chen Yu, Wang Wenquan, Lou Wenhui, Liu Liang
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200000, China.
Department of Pancreatic Surgery, Affiliated Zhongshan Hospital of Fudan University, Shanghai, 200000, China.
Heliyon. 2024 Apr 22;10(9):e29914. doi: 10.1016/j.heliyon.2024.e29914. eCollection 2024 May 15.
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 . 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.
本研究基于全基因组DNA甲基化测序技术，以识别肿瘤组织中可预测胰腺癌（PCa）患者预后的DNA甲基化生物标志物。利用TCGA数据库下载与PCa相关的DNA甲基化和转录组图谱数据。使用MethylMix软件包获得甲基化驱动基因（MDGs）。通过单因素Cox分析筛选MDGs中的候选基因与PCa患者预后的相关性，并基于关键MDGs构建预后风险评分模型。进行ROC曲线分析以评估预后风险评分模型的准确性。研究了PIK3C2B基因敲低对PCa细胞恶性表型的影响。使用178个PCa样本和171个正常样本，共鉴定出2737个差异表达基因，其中649个上调，2088个下调。利用MethylMix软件从185个TCGA PCa样本中鉴定出71个甲基化驱动基因（47个高甲基化和24个低甲基化）。Cox回归分析确定了八个与PCa预后相关的关键MDGs（LEF1、ZIC3、VAV3、TBC1D4、FABP4、MAP3K5、PIK3C2B、IGF1R）。其中七个基因高甲基化，而PIK3C2B基因低甲基化。基于这八个关键MDGs构建了预后风险预测模型，发现该模型能准确预测PCa患者的预后。此外，PIK3C2B基因敲低后，PANC - 1细胞的恶性表型降低。因此，基于八个关键MDGs的预后风险预测模型可以准确预测PCa患者的预后。
