Abstract：
BACKGROUND: With the rapid development of genomics and molecular biology, not only have biochemical indicators been used as tumour markers, but many new molecular markers have emerged. Epigenetic abnormalities are a new type of molecular marker, and DNA methylation is an important part of epigenetics.
OBJECTIVE: This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.
METHODS: The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.
RESULTS: The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.
CONCLUSIONS: The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.
OBJECTIVE: This study used weighted gene coexpression network analysis (WGCNA) to analyse key methylation-driven genes in breast cancer.
METHODS: The RNA-seq transcriptome data, DNA methylation data, and clinical information data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database, and the MethylMix R package was used to screen methylation-driven genes in breast cancer. The ClusterProfiler package and enrichplot package in R software were used to further analyse the function and signalling pathway of methylation-driven genes. Through univariate and multivariate Cox regression analyses, methylation-driver genes related to prognostic were obtained, a prognostic model was constructed and prognostic characteristics were analysed.
RESULTS: The 17 methylation-driven genes related to prognosis were obtained by the WGCNA method in breast cancer, and the prognostic significance of these methylation-driven genes was determined by transcriptome and methylation combined survival analysis. Analysis of functions and signalling pathways showed that these genes were mainly enriched in biological processes and signalling pathway. Through univariate and multivariate Cox regression analyses, a prognostic model of 5 methylation-driven genes was constructed.
CONCLUSIONS: The AUC of the receiver operating characteristic (ROC) curve of this model was 0.784, showing that the model had a good prediction effect. Based on WGCNA screening, it was found that only CDO1 was the key methylation-driven gene for prognosis in breast cancer, indicating that CDO1 may be an important indicator of the prognosis of breast cancer patients.
摘要：
背景：随着基因组学和分子生物学的快速发展，不仅生化指标被用作肿瘤标志物，但是许多新的分子标记已经出现。表观遗传异常是一种新型的分子标记,DNA甲基化是表观遗传学的重要组成部分。
目的：本研究使用加权基因共表达网络分析（WGCNA）来分析乳腺癌中关键的甲基化驱动基因。
方法：RNA-seq转录组数据，DNA甲基化数据,从癌症基因组图谱(TCGA)数据库下载乳腺癌患者的临床信息数据,MethylMixR软件包用于筛选乳腺癌中甲基化驱动基因。使用R软件中的ClusterProfiler软件包和enrichplot软件包进一步分析甲基化驱动基因的功能和信号通路。通过单变量和多变量Cox回归分析，获得与预后相关的甲基化驱动基因，构建了预后模型并分析了预后特征.
结果：通过WGCNA方法在乳腺癌中获得了与预后相关的17个甲基化驱动基因，通过转录组和甲基化联合生存分析确定这些甲基化驱动基因的预后意义。功能和信号通路分析表明,这些基因主要富集在生物过程和信号通路中。通过单变量和多变量Cox回归分析，构建了5个甲基化驱动基因的预后模型.
结论：该模型的受试者工作特征（ROC）曲线的AUC为0.784，表明该模型具有良好的预测效果。基于WGCNA筛查，研究发现，只有CDO1是乳腺癌预后的关键甲基化驱动基因，提示CDO1可能是乳腺癌患者预后的重要指标。
目的：本研究使用加权基因共表达网络分析（WGCNA）来分析乳腺癌中关键的甲基化驱动基因。
方法：RNA-seq转录组数据，DNA甲基化数据,从癌症基因组图谱(TCGA)数据库下载乳腺癌患者的临床信息数据,MethylMixR软件包用于筛选乳腺癌中甲基化驱动基因。使用R软件中的ClusterProfiler软件包和enrichplot软件包进一步分析甲基化驱动基因的功能和信号通路。通过单变量和多变量Cox回归分析，获得与预后相关的甲基化驱动基因，构建了预后模型并分析了预后特征.
结果：通过WGCNA方法在乳腺癌中获得了与预后相关的17个甲基化驱动基因，通过转录组和甲基化联合生存分析确定这些甲基化驱动基因的预后意义。功能和信号通路分析表明,这些基因主要富集在生物过程和信号通路中。通过单变量和多变量Cox回归分析，构建了5个甲基化驱动基因的预后模型.
结论：该模型的受试者工作特征（ROC）曲线的AUC为0.784，表明该模型具有良好的预测效果。基于WGCNA筛查，研究发现，只有CDO1是乳腺癌预后的关键甲基化驱动基因，提示CDO1可能是乳腺癌患者预后的重要指标。
