International Journal of General Medicine (Feb 2022)
Identification of a Nomogram with an Autophagy-Related Risk Signature for Survival Prediction in Patients with Glioma
Abstract
Xiaofeng Fu,1 Luwei Hong,2 Haiying Gong,3 Guangjuan Kan,4 Pengfei Zhang,1 Ting-Ting Cui,5 Gonglin Fan,6 Xing Si,7 Jiang Zhu1 1Department of Ultrasound, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, People’s Republic of China; 2Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, People’s Republic of China; 3Department of Ultrasound, Yiwu Traditional Chinese Medicine Hospital, Jinhua, Zhejiang, 321000, People’s Republic of China; 4Department of Ultrasound, The Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, People’s Republic of China; 5Department of Ultrasound, Taizhou Traditional Chinese Medicine Hospital, Taizhou, Zhejiang, 318000, People’s Republic of China; 6Department of Ultrasound, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, People’s Republic of China; 7Hangzhou Normal University, Hangzhou, Zhejiang, 310000, People’s Republic of ChinaCorrespondence: Jiang Zhu, Department of Ultrasound, Women’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 31000, People’s Republic of China, Tel +86 13757122629, Email [email protected]: 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.Keywords: glioma, autophagy, risk signature, prognosis, nomogram
