Development and Validation of a Radiosensitivity Prediction Model for Lower Grade Glioma Based on Spike-and-Slab Lasso

• Main Authors: Zixuan Du, Shang Cai, Derui Yan, Huijun Li, Xinyan Zhang, Wei Yang, Jianping Cao, Nengjun Yi, Zaixiang Tang
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-07-01
• Series: Frontiers in Oncology
• Subjects: lower grade gliomas; radiosensitivity prediction model; radiosensitivity; spike-and-slab lasso; lasso
• Online Access: https://www.frontiersin.org/articles/10.3389/fonc.2021.701500/full

Background and PurposeLower grade glioma (LGG) is one of the leading causes of death world worldwide. We attempted to develop and validate a radiosensitivity model for predicting the survival of lower grade glioma by using spike-and-slab lasso Cox model.MethodsIn this research, differentially expressed genes based on tumor microenvironment was obtained to further analysis. Log-rank test was used to identify genes in patients who received radiotherapy and patients who did not receive radiotherapy, respectively. Then, spike-and-slab lasso was performed to select genes in patients who received radiotherapy. Finally, three genes (INA, LEPREL1 and PTCRA) were included in the model. A radiosensitivity-related risk score model was established based on overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset that PFS as endpoint and two CGGA datasets that OS as endpoint. A novel nomogram integrated risk score with age and tumor grade was developed to predict the OS of LGG patients.ResultsWe developed and verified a radiosensitivity-related risk score model. The radiosensitivity-related risk score is served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, the nomogram integrated risk score with age and tumor grade was established to perform better for predicting 1, 3, 5-year survival rate.ConclusionsThis model can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.