N⁶-甲基腺苷(N⁶-methyladenosine, m⁶A)修饰是真核生物信使 RNA中最丰富的表观遗传修饰,其失调会导致mRNA异常生物学行为如翻译和降解紊乱,从而调控肿瘤发生发展。近期研究表明m⁶A在免疫调控过程中可发挥重要作用,其不仅可调节免疫细胞的活化,还在肿瘤微环境中免疫应答发挥重要调控作用,从而影响免疫治疗效果。越来越多的证据表明m⁶A修饰可能是肿瘤免疫治疗的重要潜在干预靶点。本文阐述了免疫细胞中m⁶A修饰调控及其在肿瘤免疫微环境中相关调节作用,并进一步探讨了靶向m⁶A调控蛋白在肿瘤免疫治疗中的干预策略及潜在治疗价值。

N⁶-methyladenosine (m⁶A) modification is the most abundant epigenetic modification in eukaryotic messenger RNA (messenger RNA). Its dysregulation drives abnormal transcription and translation processes, which promotes the occurrence and development of tumors. Studies have shown that m⁶A modification can regulate the activation of immune cells and their infiltration into the tumor microenvironment (TME), which may affect the efficiency of immunotherapy. Therefore, m⁶A modification may be a potential target for tumor immunotherapy. This paper describes the modification of m⁶A in immune cells and the antitumor immune response associated with TME, and explores the potential therapeutic value of targeting m⁶A regulators in tumor immunotherapy.

目的 探究m6A甲基化基因与卵巢癌生存预后的关系,为卵巢癌的靶向治疗、预后评估提供科学依据。方法 从TCGA及GTEx数据库中下载卵巢癌组织与正常组织mRNA表达数据进行组间差异分析,通过LASSO回归筛选与卵巢癌生存相关基因,进一步使用逐步Cox回归分析构建风险评分预测模型,根据风险评分中位数将患者分为高风险组和低风险组并使用ROC曲线下面积评价模型的预测能力。相关性分析构建与m6A基因的共表达调控网络,GO功能富集和KEGG通路分析初步探讨潜在的生物作用机制。结果 在癌组织与正常组织中发现20个m6A甲基化基因差异表达,逐步Cox回归分析筛选出3个基因(HNRNPA2B1,ZC3H13,WTAP)用于构建风险评分模型,高风险组患者的生存期较低风险组患者明显缩短(P=0.001 9),死亡风险显著增加(HR=2.643, P＜0.01),风险评分模型结合患者年龄、临床分级和分期后,1、3、5年的AUC为0.74、0.64、0.64。生物信息学分析结果提示m6A相关基因参与RNA的剪接、定位、转运、代谢调控、蛋白水解、细胞周期、核糖体合成等生物学过程。结论 成功构建卵巢癌m6A甲基化基因预后风险评估模型且该模型具备一定的预测效能。

Objective To explore the relationship between m6A methylated genes and prognosis of ovarian cancer, so as to provide scientific basis for targeted therapy and prognosis assessment of ovarian cancer. Methods The mRNA expression data of ovarian cancer tissues and normal tissues were downloaded from TCGA and GTEx databases for difference analysis between two groups. The genes related to ovarian cancer survival were screened by LASSO regression, and the risk score prediction model was further constructed by step Cox regression analysis. The patients were divided into high-risk group and low-risk group according to the median risk score, and the ROC was used for analysis. Correlation analysis was performed to construct an expression regulatory network with m6A genes, and GO function enrichment and KEGG pathway analysis were performed to preliminarily explore the potential biological mechanism. Results 20 m6A methylation genes were found in differential expression between cancer tissue and normal tissue, three genes (HNRNPA2B1, ZC3H13, WTAP) were used to construct the model through step Cox regression analysis. Patients' survivals of high-risk group were shortened than that of the low-risk group obviously (P=0.001 9), the risk of death significantly was increased (HR=2.643, P<0.01). After risk score model combined with patient age, clinical classification and stage, the AUC of 1, 3, 5 years was 0.74, 0.64 and 0.64. Bioinformatics analysis indicated that those m6A genes were involved in RNA splicing, localization, transport, metabolic regulation, proteolysis, cell cycle, ribosome synthesis and other biological processes. Conclusion The prognostic risk assessment model of m6A methylated genes for ovarian cancer was successfully constructed and the model had certain predictive efficacy.