基因表达特征可以用作不同类型癌症的预后标志物。我们的目标是在胶质瘤患者中开发一个用于预测放疗反应的基因特征。我们对辐射敏感和辐射耐药性的胶质瘤细胞系（M059J和M059K）进行了微阵列分析，以筛选出差异表达的mRNA。此外，我们从The Cancer Genome Atlas（TCGA）数据库获取了169个胶质母细胞瘤（GBM）样本和5个正常样本，以及80个GBM样本和4个正常样本来自GSE7696集。我们采用“DESeq2” R包对正常脑样本和GBM样本之间的差异表达基因（DEGs）进行了鉴定。结合从TCGA中鉴定出的与预后相关分子，我们在训练集中开发了一个辐射敏感相关预后风险标志（RRPRS），该集包括152名胶质母细胞瘤患者。随后，在包括616名TCGA数据库中的胶质瘤患者以及南方医科大学南方医院的31名胶质母细胞瘤患者的内部验证集中验证了RRPRS的可靠性。 基于微阵列和LASSO COX回归分析，我们建立了一个九基因辐射敏感相关预后风险标志。根据风险得分中位数，将胶质瘤患者分为高风险组和低风险组。Kaplan-Meier生存分析显示高风险组的无进展生存期（PFS）显著较短。签名通过时间相关的接收器操作特征曲线（ROC）分析准确预测了PFS。分层分析表明该签名特异性地预测了接受放疗患者的结果。单因素和多因素Cox回归分析表明该预测因子是胶质瘤患者预后的独立预测因子。预后图表和校正曲线显示了胶质瘤患者1、2和3年的PFS和OS。 我们的研究建立了一个新的九基因辐射敏感相关预后风险标志，可以预测接受放疗的胶质瘤患者的预后。预测图表显示该标志有很大潜力预测接受放疗的胶质瘤患者的预后。© 2023. BioMed Central Ltd., part of Springer Nature.

Gene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.Radio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The "DESeq2" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.Based on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.Our study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.© 2023. BioMed Central Ltd., part of Springer Nature.