Gao Kefei, Lian Wenqin, Zhao Rui, Huang Weiming, Xiong Jian
Department of Obstetrics and Gynaecology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
Department of Burns and Plastic & Wound Repair Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361100, China.
Transl Oncol. 2023 Aug;34:101704. doi: 10.1016/j.tranon.2023.101704. Epub 2023 May 29.
Complex outcome of ovarian cancer (OC) stems from the tumor immune microenvironment (TIME) influenced by genetic and epigenetic factors. This study aimed to comprehensively explored the subclasses of OC through lncRNAs related to both N6-methyladenosine (m6A)/N1-methyladenosine (m1A)/N7-methylguanosine (m7G)/5-methylcytosine (m5C) in terms of epigenetic variability and immune molecules and develop a new set of risk predictive systems.
The lncRNA data of OC were collected from TCGA. Spearman correlation analysis on lncRNA data of OC with immune-related gene expression and with m6A/m5C/m1A/m7G were respectively conducted. The m6A/m5C/m1A/m7G-related m6A/m5C/m1A/m7G related immune lncRNA subtypes were identified on the basis of the prognostic lncRNAs. Heterogeneity among subtypes was evaluated by tumor mutation analysis, tumor microenvironment (TME) component analysis, response to immune checkpoint blocked (ICB) and chemotherapeutic drugs. A risk predictive system was developed based on the results of Cox regression analysis and random survival forest analysis of the differences between each specific cluster and other clusters.
Three m6A/m5C/m1A/m7G-related immune lncRNA subtypes of OC showing distinct differences in prognosis, mutation pattern, TIME components, immunotherapy and chemotherapy response were identified. A set of risk predictive system consisting of 10 lncRNA for OC was developed, according to which the risk score of samples in each OC dataset was calculated and risk type was defined.
This study classified three m6A/m5C/m1A/m7G-related immune lncRNA subtypes with distinct heterogeneous mutation patterns, TME components, ICB therapy and immune response, and provided a set of risk predictive system consisted of 10 lncRNA for OC.
卵巢癌（OC）的复杂结局源于受遗传和表观遗传因素影响的肿瘤免疫微环境（TIME）。本研究旨在通过与N6-甲基腺苷（m6A）/N1-甲基腺苷（m1A）/N7-甲基鸟苷（m7G）/5-甲基胞嘧啶（m5C）相关的长链非编码RNA（lncRNA），从表观遗传变异性和免疫分子方面全面探索OC的亚类，并开发一套新的风险预测系统。
从TCGA收集OC的lncRNA数据。分别对OC的lncRNA数据与免疫相关基因表达以及与m6A/m5C/m1A/m7G进行Spearman相关性分析。基于预后lncRNA鉴定m6A/m5C/m1A/m7G相关的免疫lncRNA亚型。通过肿瘤突变分析、肿瘤微环境（TME）成分分析、对免疫检查点阻断（ICB）和化疗药物的反应评估亚型间的异质性。基于Cox回归分析结果和每个特定簇与其他簇之间差异的随机生存森林分析开发风险预测系统。
鉴定出OC的三种与m6A/m5C/m1A/m7G相关的免疫lncRNA亚型，它们在预后、突变模式、TIME成分、免疫治疗和化疗反应方面表现出明显差异，并开发了一套由10个lncRNA组成的OC风险预测系统，据此计算每个OC数据集中样本的风险评分并定义风险类型。
本研究将OC分为三种与m6A/m5C/m1A/m7G相关的免疫lncRNA亚型，它们具有明显不同的异质性突变模式、TME成分、ICB治疗和免疫反应，并提供了一套由10个lncRNA组成的OC风险预测系统。
