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Prognostic model and nomogram construction based on autophagy signatures in lower grade glioma
Author(s) -
Wang Chunhui,
Qiu Jiting,
Chen Sarah,
Li Ying,
Hu Hongkang,
Cai Yu,
Hou Lijun
Publication year - 2021
Publication title -
journal of cellular physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.529
H-Index - 174
eISSN - 1097-4652
pISSN - 0021-9541
DOI - 10.1002/jcp.29837
Subject(s) - nomogram , proportional hazards model , oncology , glioma , medicine , survival analysis , area under the curve , framingham risk score , multivariate statistics , overall survival , multivariate analysis , prognostic model , cancer research , disease , machine learning , computer science
The median survival time of lower grade glioma (LGG) tumors spans a wide range of 2–10 years and is highly dependent on the molecular characteristics and tumor location. Currently, there is no prognostic predictor for these tumors based on autophagy‐related (ATG) genes. A prognostic risk score model based on the most significant seven ATG genes was established for LGG. These seven genes, including GRID2, FOXO1, MYC, PTK6, IKBKE, BIRC5, and TP73, have been screened as potentially therapeutic targets. The Kaplan–Meier survival curve analyses validated that patients with high or low risk scores had significantly different overall survival. Following the multivariate Cox regression and area under the ROC curve (AUC) analysis, a final prognostic model based on age, World Health Organization grade, 1p19q‐codeletion status, and ATG risk score was performed as an independent prognostic indicator (training set: p = 4.09E−05, AUC = 0.901; validation set‐1: p = .00069, AUC = 0.808; validation set‐2: p = .0376, AUC = 0.830). Subsequently, a prognostic nomogram was constructed for individualized survival prediction. The calibration plots showed excellent predict efficiency between probability and actual overall survival. In this study, we provided several potential biomarkers for further developing potentially therapeutic targets of LGG. We also established a prognostic model and nomogram to improve the clinical glioma management and assist individualized survival prediction.