Zhou Min, Hong Shasha, Li Bingshu, Liu Cheng, Hu Ming, Min Jie, Tang Jianming, Hong Li
Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China.
Front Genet. 2021 Sep 9;12:675197. doi: 10.3389/fgene.2021.675197. eCollection 2021.
DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC). DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features. We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC. The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.
DNA甲基化影响多种癌症的发生、发展和预后。本研究旨在鉴定DNA甲基化差异表达基因（DEGs），并建立一种甲基化驱动的基因模型来评估卵巢癌（OC）的预后。从癌症基因组图谱、基因型-组织表达和基因表达综合数据库下载了OC患者的DNA甲基化和mRNA表达谱。我们使用R包来鉴定DNA甲基化调控的DEGs，并使用LASSO Cox回归建立预后特征。然后根据风险评分和临床病理特征绘制定量列线图。我们鉴定出56个甲基化相关的DEGs，并根据LASSO Cox回归算法构建了一个包含四个基因的预后风险特征。较高的风险评分不仅预示着预后不良，而且是一个独立的不良预后指标，这通过受试者工作特征（ROC）曲线和验证队列得到了验证。生成了一个由风险评分、年龄、国际妇产科联盟（FIGO）分期和肿瘤状态组成的列线图，以预测训练队列中3年和5年的总生存期（OS）。DNA甲基化和mRNA表达的联合生存分析表明，这两个基因可能作为OC中OS的独立预后生物标志物。发现建立的定性风险评分模型在评估OC的个体化预后和指导治疗方面具有稳健性。
