全面分析 DNA 甲基化和转录组，以鉴定子宫内膜癌中 PD-1 阴性预后性甲基化特征。

Comprehensive Analysis of DNA Methylation and Transcriptome to Identify PD-1-Negative Prognostic Methylated Signature in Endometrial Carcinoma.

机构信息

Department of Radiology, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Yuelu District, Changsha, China.

The Institute for Cell Transplantation and Gene Therapy, The Third Xiangya Hospital, Central South University, 138 Tongzipo Road, Yuelu District, Changsha, China.

出版信息

Dis Markers. 2022 May 18;2022:3085289. doi: 10.1155/2022/3085289. eCollection 2022.

DOI:10.1155/2022/3085289

PMID:35634444

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9133896/

Abstract

BACKGROUND

Epigenetic mechanism plays an important role in endometrial carcinoma (EC). This study was designed to analyze the epigenetic mechanism between DNA methylation-driven genes (DEDGs) and drugs targeting DEDGs and to develop a DEDG score model for predicting the prognosis of EC.

METHODS

Expression profile and methylation profile data of PD-1-negative EC samples were obtained from TCGA. To obtain intersected DEDGs, differentially expressed genes (DEGs) and differentially methylated genes from tumor tissues and normal tissues were analyzed by limma. A linear discriminant classification model was constructed using the gene expression profile of DMDGs, methylation profile of TSS1500, TSS200, and gene body regions. Principal component analysis (PCA) and ROC analysis were conducted. The protein-drug interactions analysis of DMDGs was performed using Network Analyst 3.0 tool. Lasso Cox regression analysis was used to screen prognostic DNA methylation driving gene and to build a risk score model. The ROC curve and Kaplan-Meier survival curve were plotted to evaluate the model prediction capability.

RESULTS

A total of 96 DMDGs were screened from the three regions, distributed on 22 chromosomes, with consistent methylation patterns in different gene regions. Both the expression profile and methylation profile of the three regions can neatly distinguish tumor samples from normal ones, with a high classifying performance. A gene signature, which consisted of ELFN1-AS1 and ZNF132, could classify EC patients into a high-risk group and low-risk group. Prognosis of the high-risk group was significantly worse than that of the low-risk group. The risk model showed a high performance in predicting the prognosis of EC.

CONCLUSION

We successfully established a risk score system with two DMDGs, which showed a high prediction accuracy of EC prognosis.

摘要

背景

表观遗传机制在子宫内膜癌（EC）中起着重要作用。本研究旨在分析 DNA 甲基化驱动基因（DEDGs）与针对 DEDGs 的药物之间的表观遗传机制，并建立 DEDG 评分模型以预测 EC 的预后。

方法

从 TCGA 获得 PD-1 阴性 EC 样本的表达谱和甲基化谱数据。通过 limma 分析肿瘤组织和正常组织的差异表达基因（DEGs）和差异甲基化基因，以获得交集 DEDGs。使用 DMDGs 的基因表达谱、TSS1500、TSS200 和基因体区域的甲基化谱构建线性判别分类模型。进行主成分分析（PCA）和 ROC 分析。使用 Network Analyst 3.0 工具对 DMDGs 的蛋白质-药物相互作用进行分析。使用 Lasso Cox 回归分析筛选预后 DNA 甲基化驱动基因并构建风险评分模型。绘制 ROC 曲线和 Kaplan-Meier 生存曲线以评估模型预测能力。

结果

从三个区域筛选出 96 个 DMDGs，分布在 22 条染色体上，不同基因区域的甲基化模式一致。三个区域的表达谱和甲基化谱都能清晰地区分肿瘤样本和正常样本，具有较高的分类性能。由 ELFN1-AS1 和 ZNF132 组成的基因特征可以将 EC 患者分为高风险组和低风险组。高风险组的预后明显差于低风险组。风险模型在预测 EC 预后方面表现出较高的性能。