Angiogenesis related genes based prognostic model of glioma patients developed by multi-omics approach

Glioma, particularly glioblastoma (GBM), is a highly malignant brain tumor with poor prognosis despite current therapeutic approaches. The tumor microenvironment (TME), plays a crucial role in glioma progression by promoting invasion and drug resistance. Angiogenesis, the formation of new blood vessels, is a tightly regulated process involving endothelial cell activation, proliferation, and migration. In cancer, angiogenesis becomes dysregulated, leading to excessive blood vessel formation.

We enrolled bulk data of TCGA-LGG/GBM, CGGA-693, and CGGA-325 cohorts, scRNA data of GSE162631, GSE84465, and GSE138794 cohorts. Identification of malignant cells was conducted by "copycat" R package. The "AUCell" R package scored the activity of target gene set of each single cell. Consensus clustering was applied using the "ConsensusClusterPlus" R package, while tumor-infiltrating immune cells were determined using "IOBR" R package. To construct a prognostic model, we used LASSO and multiCOX algorithms based on the expression levels of the 15 hub genes, the efficacy of which was verified by KM and ROC analysis.

We identified 4 different malignant cell subclusters in glioma and disclosed their distinct gene expression patterns and interactions within TME. We identified differentially expressed immune-related genes (DE-ARGs) in glioma and found 15 genes that were specifically expressed in the malignant glioma cell populations. Glioma cells with higher expression of these DE-ARGs were associated with gliogenesis, glial cell development, and vasculature development. We found that tumor-infiltrating monocytes were the main interacting cell type within glioma TME. Using the expression patterns of the 15 screened DE-ARGs, we categorized glioma samples into 2 molecular clusters with distinct immune features, suggesting a possible relationship between angiogenesis and immune activation and recruitment. We constructed a prognostic model based on the expression levels of the 15 DE-ARGs and evaluated its predictive ability for glioma patient outcomes, which displayed exceedingly high efficacy.

We characterized different malignant cell subclusters in glioma and investigate their gene expression patterns and interactions within TME. We constructed a prognostic model based on the expression levels of the 15 DE-ARGs and evaluated its predictive ability for glioma patient outcomes, which displayed exceedingly high efficacy.