Comprehensive Analysis of DNA Methylation and Transcriptome to Identify PD-1-Negative Prognostic Methylated Signature in Endometrial Carcinoma.
Comprehensive Analysis of DNA Methylation and Transcriptome to Identify PD-1-Negative Prognostic Methylated Signature in Endometrial Carcinoma.
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DOI：
10.1155/2022/3085289
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Wang, Wei
中科院分区：
文献类型：
--
作者：
Cao, Lu;Ma, Xiaoqian;Rong, Pengfei;Zhang, Juan;Yang, Min;Wang, Wei
表观遗传机制在子宫内膜癌的发生中起重要作用。本研究旨在分析DNA甲基化驱动基因（DEDG）与DEDG靶向药物的表观遗传学机制，并建立一个预测EC预后的DEDG评分模型。 从TCGA获得PD-1阴性EC样品的表达谱和甲基化谱数据。采用limma技术分析肿瘤组织和正常组织的差异表达基因和差异甲基化基因，获得差异表达基因。利用DMDGs基因表达谱、TSS 1500、TSS 200甲基化谱和基因体区域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工具进行DMDG的蛋白质-药物相互作用分析。采用Lasso考克斯回归分析筛选影响预后的DNA甲基化驱动基因，并建立风险评分模型。绘制ROC曲线和Kaplan-Meier生存曲线评价模型预测能力。 从3个区域共筛选出96个DMDG，分布在22条染色体上，不同基因区域甲基化模式一致。这三个区域的表达谱和甲基化谱都可以很好地区分肿瘤和正常组织，具有很高的分类性能。由ELFN 1-AS 1和ZNF 132组成的基因标签可以将EC患者分为高风险组和低风险组。高危组预后明显差于低危组。风险模型在预测EC预后方面表现出较高的性能。 我们成功地建立了一个风险评分系统与两个DMDG，这表明EC预后的预测准确率高。
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. 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. 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. We successfully established a risk score system with two DMDGs, which showed a high prediction accuracy of EC prognosis.
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影响因子：
14.9
作者：
Ritchie ME;Phipson B;Wu D;Hu Y;Law CW;Shi W;Smyth GK
通讯作者：
Smyth GK
影响因子：
4.7
作者：
Hamada K;Tian Y;Fujimoto M;Takahashi Y;Kohno T;Tsuta K;Watanabe SI;Yoshida T;Asamura H;Kanai Y;Arai E
通讯作者：
Arai E
影响因子：
--
作者：
Tan X;Liu S;Yao L;Cui G;Liu J;Ding J
通讯作者：
Ding J
影响因子：
5.8
作者：
Hirsh, L;Dantes, A;Amsterdam, A
通讯作者：
Amsterdam, A
DOI：
10.1016/j.omtn.2022.01.018
发表时间：
2022-03-08
期刊：
Molecular therapy. Nucleic acids
影响因子：
--
作者：
Liu J;Geng R;Ni S;Cai L;Yang S;Shao F;Bai J
通讯作者：
Bai J