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.
Keywords: angiogenesis; gene signature; glioblastoma; prognostic model; risk score.
Copyright © 2022 Wang, Hu, Liu and Zhang.