Xu Can, Cao Wei
Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
J Oncol. 2022 Aug 30;2022:3744466. doi: 10.1155/2022/3744466. eCollection 2022.
DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. . The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. . We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. . We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.
DNA甲基化是一种被广泛研究的表观遗传修饰。它与癌症的发生和发展相关，并有助于评估患者的预后。然而，大多数现有的DNA甲基化预后模型并未同时考虑下游转录组的变化。从癌症基因组图谱（TCGA）数据库下载了卵巢癌患者的RNA测序数据和DNA甲基化组学数据。使用一致性聚类加算法构建卵巢的甲基化分子亚型。采用套索回归构建多基因特征。应用独立数据集验证该特征的预后价值。使用基因集变异分析（GSVA）对与基因特征相关的通路进行富集分析。使用IMvigor 210队列探讨基因特征对免疫治疗反应的预测效力。基于DNA甲基化的组学数据，我们将卵巢癌样本分为两种预后不同的亚型。亚型之间的差异表达基因和富集分析表明，DNA甲基化与脂肪酸代谢和细胞外基质（ECM）-受体相关。此外，我们构建了一个8基因特征，在预测不同数据集和不同病理特征的卵巢癌患者预后方面被证明是有效且稳定的。最后，该8基因特征可以预测患者对免疫治疗的反应。进一步使用聚合酶链反应实验验证8个基因的表达。我们分析了卵巢癌中甲基化相关基因的预后价值。该8基因特征能很好地预测卵巢癌患者的预后和免疫治疗反应，有望在临床应用中具有价值。
