Li Xin-Yu, Zhao Zhi-Jie, Wang Jing-Bing, Shao Yu-Hao, You Jian-Xiong, Yang Xi-Tao
Department of Interventional Therapy, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Neurosurgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China.
Front Bioeng Biotechnol. 2022 May 10;10:849756. doi: 10.3389/fbioe.2022.849756. eCollection 2022.
The search for prognostic biomarkers and the construction of a prognostic risk model for hepatocellular carcinoma (HCC) based on N7-methyladenosine (m7G) methylation regulators. HCC transcriptomic data and clinical data were obtained from The Cancer Genome Atlas database and Shanghai Ninth People's Hospital, respectively. m7G methylation regulators were extracted, differential expression analysis was performed using the R software "limma" package, and one-way Cox regression analysis was used to screen for prognostic associations of m7G regulators. Using multi-factor Cox regression analysis, a prognostic risk model for HCC was constructed. Each patient's risk score was calculated using the model, and patients were divided into high- and low-risk groups according to the median risk score. Cox regression analysis was used to verify the validity of the model in the prognostic assessment of HCC in conjunction with clinicopathological characteristics. The prognostic model was built using the seven genes, namely, CYFIP1, EIF4E2, EIF4G3, GEMIN5, NCBP2, NUDT10, and WDR4. The Kaplan-Meier survival analysis showed poorer 5-years overall survival in the high-risk group compared with the low-risk group, and the receiver-operating characteristic (ROC) curve suggested good model prediction (area under the curve AUC = 0.775, 0.820, and 0.839 at 1, 3, and 5 years). The Cox regression analysis included model risk scores and clinicopathological characteristics, and the results showed that a high-risk score was the only independent risk factor for the prognosis of patients with HCC. The developed bioinformatics-based prognostic risk model for HCC was found to have good predictive power.
基于N7-甲基腺苷（m7G）甲基化调节因子的肝细胞癌（HCC）预后生物标志物的探索及预后风险模型的构建。HCC转录组数据和临床数据分别取自癌症基因组图谱数据库和上海第九人民医院。提取m7G甲基化调节因子，使用R软件“limma”包进行差异表达分析，并采用单因素Cox回归分析筛选m7G调节因子的预后相关性。通过多因素Cox回归分析构建HCC预后风险模型。使用该模型计算每位患者的风险评分，并根据中位风险评分将患者分为高风险组和低风险组。结合临床病理特征，采用Cox回归分析验证该模型在HCC预后评估中的有效性。该预后模型由CYFIP1、EIF4E2、EIF4G3、GEMIN5、NCBP2、NUDT10和WDR4这七个基因构建而成。Kaplan-Meier生存分析显示，高风险组的5年总生存率低于低风险组，受试者工作特征（ROC）曲线表明模型预测效果良好（1年、3年和5年时曲线下面积AUC分别为0.775、0.820和0.839）。Cox回归分析纳入模型风险评分和临床病理特征，结果显示高风险评分是HCC患者预后的唯一独立危险因素。基于生物信息学开发的HCC预后风险模型具有良好的预测能力。
