Yang Hao, Gao Lin, Zhang Meiling, Ning Ning, Wang Yan, Wu Di, Li Xiaomei
Department of Radiation Oncology, Inner Mongolia Cancer Hospital and The Affiliated People's Hospital of Inner Mongolia Medical University, Hohhot, China.
Institute for Endemic Fluorosis Control, Center for Endemic Disease Control, Chinese Center for Disease Control and Prevention, Harbin Medical University, Harbin, China.
Front Cell Dev Biol. 2021 Feb 23;9:644940. doi: 10.3389/fcell.2021.644940. eCollection 2021.
The deregulation of long non-coding RNAs (lncRNAs) by epigenetic alterations has been implicated in cancer initiation and progression. However, the epigenetically regulated lncRNAs and their association with clinical outcome and therapeutic response in ovarian cancer (OV) remain poorly investigated. This study performed an integrative analysis of DNA methylation data and transcriptome data and identified 419 lncRNAs as potential epigenetically regulated lncRNAs. Using machine-learning and multivariate Cox regression analysis methods, we identified and developed an epigenetically regulated lncRNA expression signature (EpiLncRNASig) consisting of five lncRNAs from the list of 17 epigenetically regulated lncRNAs significantly associated with outcome. The EpiLncRNASig could stratify patients into high-risk groups and low-risk groups with significantly different survival and chemotherapy response in different patient cohorts. Multivariate Cox regression analyses, after adjusted by other clinical features and treatment response, demonstrated the independence of the DEpiLncSig in predicting survival. Functional analysis for relevant protein-coding genes of the DEpiLncSig indicated enrichment of known immune-related or cancer-related biological pathways. Taken together, our study not only provides a promising prognostic biomarker for predicting outcome and chemotherapy response but also will improve our understanding of lncRNA epigenetic regulation mechanisms in OV.
表观遗传改变导致的长链非编码RNA(lncRNA)失调与癌症的发生和发展有关。然而,表观遗传调控的lncRNA及其与卵巢癌(OV)临床结局和治疗反应的关联仍未得到充分研究。本研究对DNA甲基化数据和转录组数据进行了综合分析,鉴定出419个lncRNA为潜在的表观遗传调控lncRNA。使用机器学习和多变量Cox回归分析方法,我们从17个与结局显著相关的表观遗传调控lncRNA列表中鉴定并开发了一个由5个lncRNA组成的表观遗传调控lncRNA表达特征(EpiLncRNASig)。EpiLncRNASig可将患者分为高风险组和低风险组,不同患者队列中的生存和化疗反应存在显著差异。在经其他临床特征和治疗反应调整后的多变量Cox回归分析表明,DEpiLncSig在预测生存方面具有独立性。对DEpiLncSig相关蛋白质编码基因的功能分析表明,已知的免疫相关或癌症相关生物途径得到了富集。综上所述,我们的研究不仅为预测结局和化疗反应提供了一个有前景的预后生物标志物,还将增进我们对OV中lncRNA表观遗传调控机制的理解。