Objective To construct a risk classifier for autophagy related genes(ARGs)to predict the survival rate of low-grade glioma(LGG)patients.Methods LGG patient and normal brain tissue data were obtained from the public databases of UCSC Xena,CGGA,GlioVis,and GTEx,and screen 232 ARGs using a human autophagy database.Differential ARGs were obtained through differential analysis.In the training set,a prognostic classifier for ARGs was constructed using univariate Cox regression analysis and LASSO regression analysis.The optimal Cut-off value was determined by drawing receiver operating characteristic(ROC)curves.The Kaplan Meier survival curve and area under the ROC curve(AUC)were used to evaluate classifier performance and validated on both internal and external datasets.Cox regression analysis was used to evaluate the independent prognostic value of classifiers.Finally,a column chart model was constructed using common clinical parameters and risk classification,and the predictive ability of the model was evaluated using ROC curves,consistency indices,and calibration curves.Results This study constructed a prognostic risk classifier consisting of six ARGs(BAG1,PTK6,EEF2,PEA15,ITGA6,and MAP1LC3C),which can classify LGG patients into high-risk and low-risk groups with significant survival differences across multiple datasets(all P＜0.05).The 5-year AUC value shows that the classifier has values of 0.837,0.755,and 0.803 in the training set,internal validation set,and TCGA total set,respectively.Meanwhile,in the external validation set,the ROC curves for 1 year,2 years,and 3 years still indicated that the classifier has good predictive accuracy.Cox regression analysis showed that the prognostic classifier had independent prognostic value in multiple datasets from TCGA(HR＞1,P＜0.05).Afterwards,a column chart model containing multiple common clinical parameters and prognostic risk classification was constructed,and the prognostic value of the model was confirmed through time dependent ROC(t-ROC),consistency index(C-index),and calibration curve analysis.Conclusion A risk classifier with high prognostic value is established consisting of 6 ARGs,and combined clinical pathological features and risk classification to construct a column chart for clinical decision-making,which can better assist clinical doctors in judging the prognosis of LGG patients and making personalized treatment clinical decisions.
lower-grade gliomaautophagy related geneprognostic classifierclinical revelance
维普
万方数据
