Department of Neurosurgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Division of Spine, Department of Orthopedics, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
Cancer Med. 2023 Aug;12(16):17445-17467. doi: 10.1002/cam4.6316. Epub 2023 Jul 11.
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.
胶质母细胞瘤（GBM）以恶性新生血管化为特征，这导致了不良的预后。然而，其机制尚不清楚。本研究旨在鉴定与 GBM 相关的预后血管生成基因及其潜在的调控机制。从癌症基因组图谱（TCGA）数据库中获得了 173 名 GBM 患者的 RNA-seq 数据，用于筛选差异表达基因（DEGs）、差异转录因子（DETFs）和反相蛋白阵列（RPPA）芯片。从血管生成相关基因集中提取差异表达基因，进行单变量 Cox 回归分析，以鉴定预后差异表达的血管生成相关基因（PDEARGs）。基于 9 个 PDEARGs（MARK1、ITGA5、NMD3、HEY1、COL6A1、DKK3、SERPINA5、NRP1、PLK2、ANXA1、SLIT2 和 PDPN）构建风险预测模型。根据风险评分将 GBM 患者分为高危组和低危组。应用 GSEA 和 GSVA 探索 GBM 血管生成相关途径的可能机制。采用 CIBERSORT 鉴定 GBM 中的免疫浸润细胞。Pearson 相关性分析评估 DETFs、PDEARGs、免疫细胞/功能、RPPA 芯片和途径之间的相关性。构建以三个 PDEARGs（ANXA1、COL6A1 和 PDPN）为中心的调控网络，展示潜在的调控机制。通过免疫组织化学（IHC）检测，对 95 名 GBM 患者的外部队列进行验证，结果显示高危 GBM 患者肿瘤组织中 ANXA1、COL6A1 和 PDPN 明显上调。单细胞 RNA 测序也验证了恶性细胞高表达 ANXA1、COL6A1、PDPN 和关键 DETFs（WWTR1）。我们基于 PDEARG 的风险预测模型和调控网络鉴定了预后生物标志物，为 GBM 血管生成的未来研究提供了有价值的见解。
