摘要
背景：多形性胶质母细胞瘤（GBM）是中枢神经系统最常见的恶性肿瘤，预后不良，疗效不理想。考虑到肿瘤和血管生成之间的高度相关性，我们试图用血管生成相关基因（ARGs）构建一个更有效的模型，以更好地预测治疗反应和预后。
方法：从NCBI基因和分子特征数据库下载ARG数据集。从TCGA和CGGA数据库中获得基因表达数据和临床信息。用R软件包“DESeq2”筛选差异表达的血管生成相关基因（DE-ARGs）。单变量Cox比例风险回归分析用于筛选与总生存率相关的ARGs。通过最小绝对收缩和选择算子（LASSO）回归分析去除多余的ARG。基于DE-ARGs的基因特征，建立了风险评分模型，并通过Kaplan–Meier分析、ROC分析等对其有效性进行了评估。
结果：GBM和正常样本之间共检测到626个DE-ARGs；31个基因被鉴定为关键的DE-ARGs。然后，建立ARG特征的风险评分。高风险评分患者的生存结果较差。事实证明，风险评分可以预测一些药物治疗的反应，如替莫唑胺化疗、放疗和免疫疗法。此外，风险评分可以作为一个有前途的预后预测指标。选择并进一步讨论了三个关键的预后基因（PLAUR、ITGA5和FMOD）。
结论：血管生成相关基因特征衍生的风险评分是预测GBM预后和治疗反应的一个很有前途的指标，有助于制定适当的治疗策略。
Abstract
Background: Glioblastoma multiforme (GBM) is the most common malignant tumor in the central nervous system with poor prognosis and unsatisfactory therapeutic efficacy. Considering the high correlation between tumors and angiogenesis, we attempted to construct a more effective model with angiogenesis-related genes (ARGs) to better predict therapeutic response and prognosis.

Methods: The ARG datasets were downloaded from the NCBI-Gene and Molecular Signatures Database. The gene expression data and clinical information were obtained from TCGA and CGGA databases. The differentially expressed angiogenesis-related genes (DE-ARGs) were screened with the R package “DESeq2”. Univariate Cox proportional hazards regression analysis was used to screen for ARGs related to overall survival. The redundant ARGs were removed by least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the gene signature of DE-ARGs, a risk score model was established, and its effectiveness was estimated through Kaplan–Meier analysis, ROC analysis, etc.

Results: A total of 626 DE-ARGs were explored between GBM and normal samples; 31 genes were identified as key DE-ARGs. Then, the risk score of ARG signature was established. Patients with high-risk score had poor survival outcomes. It was proved that the risk score could predict some medical treatments’ response, such as temozolomide chemotherapy, radiotherapy, and immunotherapy. Besides, the risk score could serve as a promising prognostic predictor. Three key prognostic genes (PLAUR, ITGA5, and FMOD) were selected and further discussed.

Conclusion: The angiogenesis-related gene signature-derived risk score is a promising predictor of prognosis and treatment response in GBM and will help in making appropriate therapeutic strategies.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8971933/