Table_14_Ovarian cancer subtypes based on the regulatory genes of RNA modifications: Novel prediction model of prognosis.xlsx

BackgroundOvarian cancer (OC) is a female reproductive system tumor. RNA modifications play key roles in gene expression regulation. The growing evidence demonstrates that RNA methylation is critical for various biological functions, and that its dysregulation is related to the progression of cancer in human.
MethodOC samples were classified into different subtypes (Clusters 1 and 2) based on various RNA-modification regulatory genes (RRGs) in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) by nonnegative matrix factorization method (NMF). Based on differently expressed RRGs (DERRGs) between clusters, a pathologically specific RNA-modification regulatory gene signature was constructed with Lasso regression. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the prognostic ability of the identified model. The correlations of clinicopathological features, immune subtypes, immune scores, immune cells, and tumor mutation burden (TMB) were also estimated between different NMF clusters and riskscore groups.
ResultsIn this study, 59 RRGs in the process of RNA modifications (m1A, m6A, m6Am, m5C, m7G, ac4C, m3C, and Ψ) were obtained from TCGA database. These RRGs were interactional, and sample clusters based on these regulators were significantly correlated with survival rate, clinical characteristics (involving survival status and pathologic stage), drug sensibility, and immune microenvironment. Furthermore, Lasso regression based on these 21 DERRGs between clusters 1 and 2 constructed a four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1). Based on this signature, 307 OC patients were classified into high- and low-risk groups based on median value of riskscores from lasso regression. This identified signature was significantly associated with overall survival, radiation therapy, age, clinical stage, cancer status, and immune cells (involving CD4+ memory resting T cells, plasma cells, and Macrophages M1) of ovarian cancer patients. Further, GSEA revealed that multiple biological behaviors were significantly enriched in different groups.
ConclusionsOC patients were classified into two subtypes per these RRGs. This study identified four-DERRG signature (ALYREF, ZC3H13, WTAP, and METTL1) in OC, which was an independent prognostic model for patient stratification, prognostic evaluation, and prediction of response to immunotherapy in ovarian cancer by classifying OC patients into high- and low-risk groups.

**背景** 卵巢癌（Ovarian Cancer, OC）是一种女性生殖系统肿瘤。RNA修饰在基因表达调控中发挥关键作用。越来越多的研究证据表明，RNA甲基化对多种生物学功能至关重要，其调控异常与人类癌症的发生发展密切相关。**方法** 卵巢癌样本基于RNA修饰过程中的各类RNA修饰调控基因（RNA-modification regulatory genes, RRGs），通过非负矩阵分解法（nonnegative matrix factorization, NMF）被划分为不同亚型（第1、2簇），涉及的RNA修饰类型包括m1A、m6A、m6Am、m5C、m7G、ac4C、m3C及Ψ。基于簇间差异表达的RRGs（differently expressed RRGs, DERRGs），借助Lasso回归构建了病理特异性的RNA修饰调控基因特征。采用Kaplan-Meier分析与受试者工作特征（receiver operating characteristic, ROC）曲线评估所构建模型的预后能力。此外，还分析了不同NMF簇与风险评分组之间的临床病理特征、免疫亚型、免疫评分、免疫细胞及肿瘤突变负荷（tumor mutation burden, TMB）的相关性。**结果** 本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中获取了参与RNA修饰过程的59个RRGs，涉及的修饰类型同前述。这些RRGs之间存在相互调控作用，基于这些调控基因划分的样本簇与患者生存率、临床特征（包括生存状态与病理分期）、药物敏感性及免疫微环境均存在显著相关性。进一步以第1、2簇间的21个DERRGs为基础，通过Lasso回归构建了由4个DERRGs组成的基因特征（ALYREF、ZC3H13、WTAP及METTL1）。基于该特征，根据Lasso回归得到的风险评分中位数，将307例卵巢癌患者划分为高风险组与低风险组。该基因特征与卵巢癌患者的总生存期、放射治疗情况、年龄、临床分期、癌症状态及免疫细胞（包括CD4+记忆静息T细胞、浆细胞及M1型巨噬细胞）均存在显著关联。此外，基因集富集分析（Gene Set Enrichment Analysis, GSEA）结果显示，不同风险组间存在多种生物学行为的显著富集差异。**结论** 基于上述RRGs，卵巢癌患者可被划分为两种亚型。本研究在卵巢癌中鉴定出了由4个DERRGs组成的基因特征（ALYREF、ZC3H13、WTAP及METTL1），该特征可作为独立的预后模型，通过将卵巢癌患者划分为高、低风险组，实现患者分层、预后评估及免疫治疗应答预测。