摘要
出身背景
胶质瘤是中枢神经系统中一种常见的肿瘤，其发病率和死亡率都很高。自噬在神经胶质瘤的发展和进展中起着至关重要的作用，并参与正常的生理和各种病理生理进展。
患者和方法
从三个公共数据库中共获得531个自噬相关基因（ARGs），并收集了1738名神经胶质瘤患者。我们进行了最小绝对收缩和选择算子回归，以确定最佳预后相关基因，并构建了自噬相关风险特征。通过受试者操作特征分析、生存分析、临床相关性分析和Cox回归验证了签名的性能。采用多元Cox回归分析建立列线图模型。Schoenfeld的全局和个体检验用于估计Cox比例风险回归分析假设的时变协方差。使用R编程语言作为主要的数据分析和可视化工具。
后果
构建了一个由15个ARG组成的总体生存相关风险特征，并将神经胶质瘤患者显著分为高风险组和低风险组（P<0.0001）。1年、3年和5年生存率的ROC曲线下面积分别为0.890、0.923和0.889。单变量和多变量Cox分析表明，风险特征是一个令人满意的独立预后因素。此外，构建了一个将风险特征与临床信息相结合的诺模图模型，用于预测神经胶质瘤患者的生存率（C指数=0.861±0.024）。
结论
本研究构建了一个新的、可靠的ARG相关风险信号，该信号被证实是一个令人满意的预后标志。诺模图模型可以为单独预测每一位神经胶质瘤患者的预后和促进最佳治疗的选择提供参考。
Abstract
Background
Glioma is a common type of tumor in the central nervous system characterized by high morbidity and mortality. Autophagy plays vital roles in the development and progression of glioma, and is involved in both normal physiological and various pathophysiological progresses.

Patients and Methods
A total of 531 autophagy-related genes (ARGs) were obtained and 1738 glioma patients were collected from three public databases. We performed least absolute shrinkage and selection operator regression to identify the optimal prognosis-related genes and constructed an autophagy-related risk signature. The performance of the signature was validated by receiver operating characteristic analysis, survival analysis, clinic correlation analysis, and Cox regression. A nomogram model was established by using multivariate Cox regression analysis. Schoenfeld’s global and individual test were used to estimate time-varying covariance for the assumption of the Cox proportional hazard regression analysis. The R programming language was used as the main data analysis and visualizing tool.

Results
An overall survival-related risk signature consisting of 15 ARGs was constructed and significantly stratified glioma patients into high- and low-risk groups (P < 0.0001). The area under the ROC curve of 1-, 3-, 5-year survival was 0.890, 0.923, and 0.889, respectively. Univariate and multivariate Cox analyses indicated that the risk signature was a satisfactory independent prognostic factor. Moreover, a nomogram model integrating risk signature with clinical information for predicting survival rates of patients with glioma was constructed (C-index=0.861±0.024).

Conclusion
This study constructed a novel and reliable ARG-related risk signature, which was verified as a satisfactory prognostic marker. The nomogram model could provide a reference for individually predicting the prognosis for each patient with glioma and promoting the selection of optimal treatment.

https://www.tandfonline.com/doi/full/10.2147/IJGM.S335571