Shi Huadi, Zhong Fulan, Yi Xiaoqiong, Shi Zhenyi, Ou Feiyan, Xu Zumin, Zuo Yufang
Cancer Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Front Genet. 2021 Feb 9;11:616998. doi: 10.3389/fgene.2020.616998. eCollection 2020.
Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature. ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability. Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable. A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.
自噬在癌症发展中起重要作用。然而，自噬相关基因（ARGs）在宫颈癌（CC）中的预后价值尚不清楚。本研究的目的是基于ARGs特征构建一个预测CC患者预后的生存模型。ARGs从人类自噬数据库和分子特征数据库中获取。ARGs的表达谱和临床数据从TCGA数据库下载。使用R软件对CC组织和正常组织进行差异表达分析，以筛选出表达异常的ARGs。采用单因素Cox、Lasso和多因素Cox回归分析构建预后模型，并通过测试集和全集进行验证。我们还对CC的风险评分和一些临床病理因素进行了独立预后分析。最后，建立了一个临床实用的列线图来预测个体生存概率。与正常组织相比，CC组织中有63个ARGs表达异常。通过Lasso和Cox回归分析最终获得了基于3个ARGs的风险模型。在训练集和验证集中，高风险患者的总生存期（OS）均显著短于低风险患者。ROC曲线验证了其在生存预测中的良好性能，表明该模型具有一定程度的敏感性和特异性。多因素Cox分析表明，风险评分是一个独立的预后因素。最后，我们绘制了一个列线图来预测CC患者1年、3年和5年的生存率。校准曲线表明该模型可靠。成功构建了基于CHMP4C、FOXO1和RRAGB的风险预测模型，该模型可有效预测CC患者的预后。该模型可为CC患者制定精准治疗策略提供参考。
