实用肿瘤学杂志 ›› 2023, Vol. 37 ›› Issue (1): 39-45.doi: 10.11904/j.issn.1002-3070.2023.01.007

• 临床研究 • 上一篇    下一篇

肺腺癌m6A甲基化调控因子相关预后风险模型的构建及临床意义

夏前林, 仇荣, 陆悦, 张琼, 杜玉珍   

  1. 上海交通大学附属第六人民医院检验科(上海 201306)
  • 收稿日期:2022-08-26 修回日期:2022-12-28 出版日期:2023-02-28 发布日期:2023-03-21
  • 通讯作者: 杜玉珍,E-mail:duyuzhen2005@163.com
  • 作者简介:夏前林,男,(1990-),硕士,检验技师,从事肿瘤生化与分子诊断的研究。
  • 基金资助:
    国家自然科学基金(编号:81974315);浦东新区科技发展基金(编号:PKJ2021-Y53)

Construction and clinical significance of prognostic risk model related to m6A methylation regulators in lung adenocarcinoma

XIA Qianlin, QIU Rong, LU Yue, ZHANG Qiong, DU Yuzhen   

  1. Department of Clinical Laboratory,Shanghai Jiao Tong University Affiliated Sixth People′s Hospital,Shanghai 201306,China
  • Received:2022-08-26 Revised:2022-12-28 Online:2023-02-28 Published:2023-03-21

摘要: 目的 构建基于N6-甲基腺苷(N6-methyladenosine,m6A)甲基化调控因子的肺腺癌预后风险模型,为肺腺癌的预后评估提供科学依据。方法 从TCGA数据库下载mRNA表达和临床数据。使用R软件对24种m6A甲基化调控因子进行差异表达分析。利用单因素Cox回归分析初步筛选出与生存相关的m6A调控因子,在此基础上通过Lasso回归进一步筛选纳入模型的变量,将Lasso回归得到的m6A调控因子用于构建Cox回归模型并计算每个样本的风险评分。qRT-PCR检测模型基因HNRNPC及IGF2BP1在正常肺上皮细胞(REAS-2B)及肺腺癌细胞株(A549、H1299、PC9)中的表达。采用TCGA数据库中配对组织的差异分析检测模型基因在肺腺癌及正常肺组织的差异表达。使用Kaplan-Meier生存曲线及ROC曲线对模型效能进行评估。通过热图分析不同风险组的临床特征,并结合其他临床参数进行单因素和多因素Cox回归分析对风险模型的独立预后性进行检验。结果 19个m6A甲基化调控因子在肺腺癌和正常组织中显著差异表达。单因素Cox回归分析发现4个肺腺癌生存显著相关的m6A调控因子(P<0.01),进一步采用Lasso回归联合Cox回归算法构建了包含2个基因(IGF2BP1、HNRNPC)的预后风险模型。qRT-PCR结果显示HNRNPC和IGF2BP1相对表达量在肺腺癌细胞株A549、H1299和PC-9中高于正常肺上皮细胞REAS-2B,差异具有统计学意义(P<0.01)。TCGA数据库的59对肺腺癌及相邻的正常肺组织的匹配差异分析发现HNRNPC和IGF2BP1在肺腺癌中表达高于正常肺组织,差异具有统计学意义(P<0.001)。生存曲线分析显示相比低风险组患者,高风险患者总体生存期明显降低(P<0.01)。ROC曲线结果表明模型可以较好地预测肺腺癌患者预后(AUC=0.724)。多因素Cox回归分析显示该预后风险模型可作为独立预后因素(HR=2.357,P<0.001)对肺腺癌患者的预后进行预测。结论 本研究构建了基于m6A甲基化调控因子的预后风险评估模型,且该模型具有较好的预测能力,对制定合理及有效的个体化治疗方案具有潜在的参考价值。

关键词: N6-甲基腺苷, 预后模型, 风险评分, 肺腺癌

Abstract: Objective The Objective of this study was to construct a prognostic risk model for lung adenocarcinoma based on N6-methyladenosine(m6A)regulators,and to provide a scientific basis for the prognostic evaluation of lung adenocarcinoma. Methods The mRNA expression and clinical data were downloaded from TCGA database.Differential expression analysis of the 24 methylation regulatory factors was performed using R software.Univariate Cox regression analysis was used to initially screen out m6A regulators associated with lung adenocarcinoma survival,on which variables included in the model were further screened by Lasso regression.The m6A regulators obtained from Lasso regression was used to construct the Cox regression model and to calculate the risk score for each sample.The expression of model genes IGF2BP1 and HNRNPC in normal lung epithelial cells(REAS-2B)and lung adenocarcinoma cell lines(A549 cells,H1299 cells and PC-9 cells)were detected by qRT-PCR(P<0.01).Differential analysis of the paired tissues in TCGA database was used to detect the differential expression of the model genes in lung adenocarcinoma and normal lung tissues(P<0.001).The model performance was assessed using the Kaplan-Meier survival curves and the ROC curve.The clinical characteristics of different risk groups were analyzed by a heatmap,and the independent prognosis of risk models was tested by univariate and multivariate Cox regression analysis combined other clinical parameters. Results Nineteen m6A methylation regulators were significantly differentially expressed in lung adenocarcinoma and normal tissues.Univariate regression analysis found four significant m6A-related regulatory factors for the prognosis of lung adenocarcinoma(P<0.01),and a prognostic risk model including two genes(IGF2BP1 and HNRNPC)was constructed using Lasso and Cox regression regression algorithm.The results of qRT-PCR showed that the relative expression of IGF2BP1 and HNRNPC was higher in A549 cells,H1299 cells and PC-9 cells than that in REAS-2B cells,and the difference was statistically significant(P<0.001).Matched difference analysis for 59 pairs of lung adenocarcinoma and adjacent normal lung tissues from TCGA found that the expression of IGF2BP1 and HNRNPC in lung adenocarcinoma was higher than that in normal lung tissues,and the difference was significant difference(P<0.001).Survival curve analysis showed that the survival time of high-risk patients was significantly shorter than that of low-risk patients(P<0.01).The ROC curve results showed that the model could better predict the prognosis of lung adenocarcinoma patients(AUC=0.724).Multivariate Cox regression showed that the prognostic risk model could be used as an independent prognostic factor(HR=2.357,P<0.001). Conclusion In this study,a prognostic risk assessment model based on m6A methylation regulators was constructed,and the model has good predictive ability,which has potential reference value for formulating reasonable and effective individualized treatment plan.

Key words: N6-methyladenosine, Prognostic model, Risk score, Lung adenocarcinoma

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