PO.TB01.01 · 肿瘤生物学
血管生成相关基因在胶质瘤中的预后相关性：IDH/1p19q状态的影响及抗血管生成免疫治疗策略的机会
Prognostic relevance of angiogenesis-associated genes in gliomas: Influence of idh/1p19q status and opportunities for antiangiogenic immunotherapy strategies
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：血管生成，即新血管的形成，是胶质瘤进展的核心，并支持肿瘤生长和侵袭。早期识别低级别胶质瘤（LGG）中的侵袭性生物学特征对于指导治疗决策至关重要。我们开发了一种免疫导向的血管生成基因特征，用于表征LGG和胶质母细胞瘤（GBM）中的侵袭性肿瘤表型。本研究还评估了其与关键胶质瘤生物标志物（包括IDH突变和1p/19q共缺失）的关联，并评估了其在两种肿瘤类型中的预后相关性。
目的：开发并验证一种免疫导向的血管生成基因特征，用于识别侵袭性胶质瘤生物学特征、预测LGG和GBM患者的预后，并评估其与IDH突变和1p/19q共缺失状态的关系。
方法：我们分析了来自癌症基因组图谱（TCGA）的LGG和GBM样本。使用EPIC估计免疫浸润水平，并将样本分为高浸润组和低浸润组。识别这些组之间的差异表达基因，随后进行加权基因共表达网络分析（WGCNA），将共表达基因聚类为模块。使用Fisher精确检验评估WGCNA模块基因在Hallmark血管生成基因集中的富集情况，优先考虑重叠基因。使用Cox回归、Kaplan-Meier分析和时间依赖性ROC曲线评估与IDH状态、1p/19q共缺失和患者生存的关联。在中国胶质瘤基因组图谱（CGGA）和两个单细胞数据集（pLGG：GSE222850；GBM：GSE138794）中进行验证。
结果：所得的免疫导向血管生成特征与IDH突变和1p/19q亚型显著相关（校正后p ≤ 0.05），并强有力地预测了患者结局。较高的特征表达与较差的生存相关，且在多变量分析中仍具有独立的预后价值（例如，TCGA LGG p = 3.28×10⁻⁷；CGGA LGG p = 1.08×10⁻⁸）。该特征在时间依赖性ROC分析中显示出较强的预测准确性（TCGA LGG AUC：1年0.959，3年0.873，5年0.812）。在LGG中观察到的基因表达模式呈现出与某些GBM亚组一致的渐进趋势。在单细胞数据集中，大多数基因在高、低免疫浸润组之间显示出显著的差异表达（校正后p ≤ 0.05）。
结论：我们识别并验证了一种整合了胶质瘤遗传、分子和临床特征的免疫导向血管生成基因特征。该特征在LGG和GBM中均显示出较强的预后价值，并反映了沿胶质瘤进展逐渐增强的血管生成活性。该基因集可作为患者风险分层的实用工具，并为未来的治疗策略提供潜在靶点。
查看英文原文 English abstract
Background: Angiogenesis, the formation of new blood vessels, is central to glioma progression and supports tumor growth and invasion. Early identification of aggressive biology in lower-grade gliomas (LGG) is essential for guiding treatment decisions. We developed an immune-oriented angiogenesis gene signature that characterizes aggressive tumor phenotypes in LGG and glioblastoma (GBM). The study also evaluates its association with key glioma biomarkers, including IDH mutations and 1p/19q co-deletions, and assesses its prognostic relevance across both tumor types.
Objective: To develop and validate an immune-oriented angiogenesis gene signature that identifies aggressive glioma biology, predicts patient prognosis in LGG and GBM, and evaluates its relationship with IDH mutation and 1p/19q co-deletion status.
Methods: We analyzed LGG and GBM samples from The Cancer Genome Atlas (TCGA). Immune infiltration levels were estimated using EPIC, and samples were classified into high and low infiltration groups. Differentially expressed genes between these groups were identified, followed by Weighted Gene Co-expression Network Analysis (WGCNA) to cluster co-expressed genes into modules. Fisher's exact test was used to assess enrichment of WGCNA module genes within the Hallmark Angiogenesis gene set, prioritizing overlapping genes. Associations with IDH status, 1p/19q co-deletion, and patient survival were evaluated using Cox regression, Kaplan-Meier analysis, and time-dependent ROC curves. Validation was performed in the Chinese Glioma Genome Atlas (CGGA) and two single-cell datasets (pLGG: GSE222850; GBM: GSE138794).
Results: The derived immune-oriented angiogenesis signature was significantly associated with IDH mutation and 1p/19q subtypes (Adj. p ≤ 0.05) and strongly predicted patient outcomes. Higher signature expression correlated with poorer survival and remained independently prognostic in multivariate analyses (e.g., TCGA LGG p = 3.28×10⁻⁷; CGGA LGG p = 1.08×10⁻⁸). The signature showed strong predictive accuracy in time-dependent ROC analyses (TCGA LGG AUCs: 1-year 0.959, 3-year 0.873, 5-year 0.812). Gene expression patterns observed in LGG showed a progressive trend consistent with certain GBM subgroups. In single-cell datasets, most genes showed significant differential expression between high and low immune infiltration groups (Adj. p ≤ 0.05).
Conclusion: We identified and validated an immune-oriented angiogenesis gene signature that integrates genetic, molecular, and clinical features across gliomas. The signature demonstrates strong prognostic value in both LGG and GBM and reflects increasing angiogenic activity along glioma progression. This gene set may serve as a practical tool for patient risk stratification and offers potential targets for future therapeutic strategies.
利益披露 Disclosure
P. Das, None.