AUTHOR=Du Zixuan , Liu Hanshan , Bai Lu , Yan Derui , Li Huijun , Peng Sun , Cao JianPing , Liu Song-Bai , Tang Zaixiang 

TITLE=A Radiosensitivity Prediction Model Developed Based on Weighted Correlation Network Analysis of Hypoxia Genes for Lower-Grade Glioma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.757686

DOI=10.3389/fonc.2022.757686

ISSN=2234-943X

ABSTRACT=Background and Purpose: Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there was not radiosensitivity prediction model was not construct based on hypoxia genes. We attempted to a radiosensitivity prediction model developed based on hypoxia genes for lower grade glioma by using weighted correlation network analysis (WGCNA) and lasso.
Methods: In this research, radiotherapy-related module genes were selected after WGCNA. Then, lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, UROS) was included in the model. A radiosensitivity-related risk score model was established based on the overall rate of TCGA dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two CGGA datasets. A novel nomogram was developed to predict the OS of LGG patients.
Results: GO analysis shown that radiotherapy-related module genes were related to hypoxia biological process. We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score was 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 rates.
Conclusions: We developed and validated a radiosensitivity prediction model, which can be used by clinicians and researchers to predict patient’s survival rates and achieve personalized treatment of LGG.