m6A甲基化基因风险评估模型在卵巢癌预后的临床意义

doi:10.3969/j.issn.1000-8535.2021.03.001

摘要/Abstract

摘要： 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。

关键词: m6A, 卵巢癌, 风险预测模型, 预后

Abstract: 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.

Key words: m6A, Ovarian cancer, Risk score prediction model, Prognosis

古嘉基, 连泳欣, 丘福满. m6A甲基化基因风险评估模型在卵巢癌预后的临床意义[J]. 广州医药, 2021, 52(3): 1-8.

GU Jiaji, LIAN Yongxin, QIU Fuman. The significance of m6A genes risk model in the prognosis of ovarian cancer[J]. Guangzhou Medical Journal, 2021, 52(3): 1-8.

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