Du Zixuan, Liu Hanshan, Bai Lu, Yan Derui, Li Huijun, Peng Sun, Cao JianPing, Liu Song-Bai, Tang Zaixiang
Department of Biostatistics and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Medical College of Soochow University, Suzhou, China.
Suzhou Key Laboratory of Medical Biotechnology, Suzhou Vocational Health College, Suzhou, China.
Front Oncol. 2022 Feb 25;12:757686. doi: 10.3389/fonc.2022.757686. eCollection 2022.
Hypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso).
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 (, , , , , , , , , , , and ) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients.
We developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates.
We developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.
缺氧是实体瘤物理微环境的基本特征之一。放疗与缺氧之间的关系较为复杂。然而，目前尚无基于缺氧基因的放射敏感性预测模型。我们试图通过加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（Lasso）构建一种基于缺氧基因的低级别胶质瘤（LGG）放射敏感性预测模型。
在本研究中，通过WGCNA筛选出放疗相关模块基因。然后，对接受放疗的患者进行Lasso分析以选择基因。最终，12个基因（，，，，，，，，，，，和）被纳入模型。基于接受放疗患者的癌症基因组图谱（TCGA）数据集的总发生率建立了放射敏感性相关风险评分模型。该模型在TCGA数据集和两个中国胶质瘤基因组图谱（CGGA）数据集中进行了验证。开发了一种新的列线图来预测LGG患者的总生存期。
我们开发并验证了一种基于缺氧基因的放射敏感性相关风险评分模型。该放射敏感性相关风险评分可作为独立的预后指标。此放射敏感性相关风险评分模型具有预后预测能力。此外，建立了一个将风险评分与年龄和肿瘤分级相结合的列线图，用于预测1年、3年和5年生存率时表现更佳。
我们开发并验证了一种放射敏感性预测模型，临床医生和研究人员可利用该模型预测患者生存率并实现LGG的个体化治疗。
