Liu Yun, Zhao Yufei, Fang Jinmei, Fang Jing, Yuan Xiaodong
Department of Radiation Oncology, Anhui Provincial Cancer Hospital, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Organ Transplant Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Transl Cancer Res. 2020 Dec;9(12):7495-7504. doi: 10.21037/tcr-20-2476.
Immune and stromal cells are the two major non-tumor cell types in the glioblastoma (GBM) microenvironment, which play critical roles in the prognostic assessment of tumors. Previous findings have identified genes with prognostic value in the GBM microenvironment; however, correlations between microenvironment-related genes and GBM radioresistance remain unclear. Therefore, in this study, we screened for vital microenvironment-related genes associated with radioresistance in GBM.
We analyzed the data from 348 patients with primary GBM that had undergone radiotherapy (patients with GBM-RT), in The Cancer Genome Atlas database. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm was used to calculate stromal and immune scores to identify the differentially expressed genes (DEGs). Functional enrichment analyses and a protein-protein interaction (PPI) network construction were performed. Survival analysis was conducted to determine genes with prognostic value. The Chinese Glioma Genome Atlas (CGGA) cohort was utilized for validation.
The stromal score was significantly correlated with the prognoses of patients with GBM-RT. Based on the stromal and immune scores, 139 common DEGs involved in inflammation or immune-related activities were identified. We also identified 86 DEGs associated with poor prognosis, which further intersected with the top nodes in the PPI network. Finally, we identified the shared DEGs using the CGGA database and found 10 genes with prognostic value that contributed to GBM radioresistance. These genes included , and .
We identified several genes related to the immune microenvironment that may mediate GBM radioresistance. Our findings provide a theoretical basis for predicting the radioresponse and survival of patients with GBM.
免疫细胞和基质细胞是胶质母细胞瘤（GBM）微环境中的两种主要非肿瘤细胞类型，它们在肿瘤的预后评估中起着关键作用。先前的研究已经确定了在GBM微环境中具有预后价值的基因；然而，微环境相关基因与GBM放射抗性之间的相关性仍不清楚。因此，在本研究中，我们筛选了与GBM放射抗性相关的重要微环境相关基因。
我们分析了癌症基因组图谱数据库中348例接受过放疗的原发性GBM患者（GBM-RT患者）的数据。使用肿瘤组织中基因表达数据评估基质和免疫细胞（ESTIMATE）算法来计算基质和免疫评分，以鉴定差异表达基因（DEG）。进行了功能富集分析和蛋白质-蛋白质相互作用（PPI）网络构建。进行生存分析以确定具有预后价值的基因。利用中国胶质瘤基因组图谱（CGGA）队列进行验证。
基质评分与GBM-RT患者的预后显著相关。基于基质和免疫评分，确定了139个参与炎症或免疫相关活动的常见DEG。我们还鉴定了86个与预后不良相关的DEG，这些DEG进一步与PPI网络中的顶级节点相交。最后，我们使用CGGA数据库确定了共享的DEG，并发现了10个具有预后价值的基因，这些基因促成了GBM的放射抗性。这些基因包括 ，以及 。
我们鉴定了几个与免疫微环境相关的基因，它们可能介导GBM的放射抗性。我们的研究结果为预测GBM患者的放射反应和生存提供了理论依据。
