Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China.
BMC Med Genomics. 2022 May 7;15(1):105. doi: 10.1186/s12920-022-01256-2.
Breast cancer (BC) is the leading cause of death among women, and epigenetic alterations that can dysregulate long noncoding RNAs (lncRNAs) are thought to be associated with cancer metabolism, development, and progression. This study investigated the epigenetic regulation of lncRNAs and its relationship with clinical outcomes and treatment responses in BC in order to identify novel and effective targets for BC treatment.
We comprehensively analysed DNA methylation and transcriptome data for BC and identified epigenetically regulated lncRNAs as potential prognostic biomarkers using machine learning and multivariate Cox regression analysis. Additionally, we applied multivariate Cox regression analysis adjusted for clinical characteristics and treatment responses to identify a set of survival-predictive lncRNAs, which were subsequently used for functional analysis of protein-encoding genes to identify downstream biological pathways.
We identified a set of 1350 potential epigenetically regulated lncRNAs and generated a methylated lncRNA dataset for BC, MylnBrna, comprising 14 lncRNAs from a list of 20 epigenetically regulated lncRNAs significantly associated with tumour survival. MylnBrna stratifies patients into high-risk and low-risk groups with significantly different survival rates. These lncRNAs were found to be closely related to the biological pathways of amino acid metabolism and tumour metabolism, revealing a potential tumour-regulation function.
This study established a potential prognostic biomarker model (MylnBrna) for BC survival and offered an insight into the epigenetic regulatory mechanisms of lncRNAs in BC in the context of tumour metabolism.
乳腺癌（BC）是女性死亡的主要原因，人们认为表观遗传改变可以使长链非编码 RNA（lncRNA）失调，与癌症代谢、发展和进展有关。本研究旨在调查 lncRNA 的表观遗传调控及其与 BC 临床结局和治疗反应的关系，以确定 BC 治疗的新的有效靶点。
我们全面分析了 BC 的 DNA 甲基化和转录组数据，使用机器学习和多变量 Cox 回归分析鉴定了受表观遗传调控的 lncRNA 作为潜在的预后生物标志物。此外，我们还应用了多变量 Cox 回归分析，调整了临床特征和治疗反应，以鉴定一组生存预测 lncRNA，随后用于对编码蛋白的基因进行功能分析，以鉴定下游生物学途径。
我们确定了一组 1350 个潜在的受表观遗传调控的 lncRNA，并生成了一个用于 BC 的甲基化 lncRNA 数据集 MylnBrna，其中包含 20 个与肿瘤生存显著相关的受表观遗传调控的 lncRNA 中的 14 个 lncRNA。MylnBrna 将患者分为高风险和低风险组，两组的生存率有显著差异。这些 lncRNA 与氨基酸代谢和肿瘤代谢的生物学途径密切相关，揭示了其潜在的肿瘤调节功能。
本研究建立了一个用于 BC 生存的潜在预后生物标志物模型（MylnBrna），并深入了解了 lncRNA 在肿瘤代谢背景下对 BC 的表观遗传调控机制。
