Identification of angiogenesis‐related genes signature for predicting survival and its regulatory network in glioblastoma

Abstract

Glioblastoma (GBM) is notorious for malignant neovascularization that contributes to undesirable outcome. However, its mechanisms remain unclear. This study aimed to identify prognostic angiogenesis‐related genes and the potential regulatory mechanisms in GBM. RNA‐sequencing data of 173 GBM patients were obtained from the Cancer Genome Atlas (TCGA) database for screening differentially expressed genes (DEGs), differentially transcription factors (DETFs), and reverse phase protein array (RPPA) chips. Differentially expressed genes from angiogenesis‐related gene set were extracted for univariate Cox regression analysis to identify prognostic differentially expressed angiogenesis‐related genes (PDEARGs). A risk predicting model was constructed based on 9 PDEARGs, namely MARK1, ITGA5, NMD3, HEY1, COL6A1, DKK3, SERPINA5, NRP1, PLK2, ANXA1, SLIT2, and PDPN. Glioblastoma patients were stratified into high‐risk and low‐risk groups according to their risk scores. GSEA and GSVA were applied to explore the possible underlying GBM angiogenesis‐related pathways. CIBERSORT was employed to identify immune infiltrates in GBM. The Pearson's correlation analysis was performed to evaluate the correlations among DETFs, PDEARGs, immune cells/functions, RPPA chips, and pathways. A regulatory network centered by three PDEARGs (ANXA1, COL6A1, and PDPN) was constructed to show the potential regulatory mechanisms. External cohort of 95 GBM patients by immunohistochemistry (IHC) assay demonstrated that ANXA1, COL6A1, and PDPN were significantly upregulated in tumor tissues of high‐risk GBM patients. Single‐cell RNA sequencing also validated malignant cells expressed high levels of the ANXA1, COL6A1, PDPN, and key DETF (WWTR1). Our PDEARG‐based risk prediction model and regulatory network identified prognostic biomarkers and provided valuable insight into future studies on angiogenesis in GBM.

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis

“…Additionally, immunohistochemistry assays demonstrated that COL6A1 was significantly upregulated in tumor tissues of high-risk GBM patients. Their single-cell RNA sequencing work also validated those malignant cells expressed high levels of COL6A1 (Wan et al, 2023). Altieri et al (2019) identified a network of gene mutations associated with survival, including COL5A3 and LAMA2 (p < 0.05).…”

“…Additionally, immunohistochemistry assays demonstrated that COL6A1 was significantly upregulated in the tumor tissues of high-risk GBM patients. Single-cell RNA sequencing also validated that these malignant cells expressed high levels of COL6A1 [60]. Increased expression of COL6A1 and the vascular endothelial molecular marker cluster of differentiation ( CD31) induced glioma angiogenesis [61].…”

“…Additionally, high expression of ITGA3 in GBM significantly and independently predicts poor survival in patients with astrocytoma [86]. Similar to COL6A1 , the ITGA5 gene was included in a risk prediction model constructed based on differentially expressed angiogenesis-related genes [60], and this risk score could also serve as a promising prognostic predictor [87].…”

Overcoming challenges in glioblastoma treatment: targeting infiltrating cancer cells and harnessing the tumor microenvironment

“…Blood-Brain Barrier's pericyte's disruption and hypoxia sustain small vessel proliferation and, in the small vessels district, the dialogue between cancer cells proteins such Nestin and CD133 and endothelial biomarkers such as CD34 proteins facilitates brain metastasis, sustaining tumor cells circulation and subsequent tissue infiltration (Schiffer et al, 2018). More recently, the development of vascularization in GB was also described using genomic analytical tools, to create a risk prediction model for GB, where prognostic differentially expressed angiogenesis-related genes (PDEARGs) become potentially druggable prognostic biomarkers of regulatory networks and provide valuable insight to tackle the role of neovascularization in these tumors (Wan et al, 2023).…”