Development, validation, and updating of prognostic models for m7G- associated genes in lower-grade gliomas

Abstract

Abstract Background Studies are aiming at developing prognostic models using N7-methylguanosine (m7G)-related genes in gliomas, however, models with good predictive performance for lower-grade gliomas have yet to be developed. Methods Based on genes with m7G variants and clinical information, two prediction models have been derived to predict the probability of survival for patients with lower-grade gliomas in TCGA. The models were externally validated using independent datasets. Based on CGGA information, updated models that were created matched the features of the local population. Results Two models were derived, validated and updated. Model 1, which was derived on the basis of mRNA, only contains five genes: CD37, EIF3A, CALU, COLGALT1, and DDX3X. Model 2 included six variables: grade, age, gender, IDH mutation status, 1p/19q codeletion status and prognostic index of model 1. The C-statistic of revised model 1 was 0.764 (95%CI: 0.730–0.798) in the revised set and 0.700 (95%CI: 0.658–0.742) in the test set. Regarding internal validation, C-statistic for model 2 with 1000 bootstrap replications was 0.848, while in external validation, the C-statistic was 0.752 (95%CI: 0.714–0.788). Both models exhibited satisfactory calibration after updating in external validation. The models' web calculator is provided at https://lhj0520.shinyapps.io/M7G-LGG_model/. Conclusion we developed and validated two models and updated them, which makes the models better predictors for patients.