Zhang Binbin, Cheng Yaling, Li Ruichun, Lian Minxue, Guo Shiwen, Liang Chen
Department of Neurosurgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Department of Radiology Medical Physics, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Transl Cancer Res. 2023 Jan 30;12(1):13-30. doi: 10.21037/tcr-22-1592. Epub 2022 Dec 22.
Long noncoding RNA (lncRNA) can regulate tumorigenesis, angiogenesis, proliferation, and other tumor biological behaviors, and is closely related to the growth and progression of glioma. The purpose of this research was to investigate the role of angiogenesis-related lncRNA in the prognosis and immunotherapy of glioblastoma multiforme (GBM).
Differential analysis was carried out to acquire angiogenesis-related differentially expressed lncRNAs (AR-DElncRNAs). The AR-DElncRNAs were then subjected to univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Based on the median risk score, patients were classified into high-risk and low-risk groups. Kaplan-Meier survival analysis was conducted to estimate the prognostic value of the prognostic model. In addition, a nomogram was built to predict individual survival probabilities by combining clinicopathological characteristics and a prognostic model. Furthermore, immune infiltration, immunotherapy, and drug sensitivity analyses were administered to investigate the differences between the high- and low-risk groups.
We identified 3 lncRNAs (DGCR5, PRKAG2-AS1, and ACAP2-IT1) that were significantly associated with the survival of GBM patients from the 255 AR-DElncRNAs based on univariate Cox and LASSO analyses. Then, a prognostic model was structured according to these 3 lncRNAs, from which we found that high-risk GBM patients had a worse prognosis than that of low-risk patients. Moreover, the risk score was determined to be an independent prognostic factor [hazard ratio (HR) =1.444; 95% confidence interval (CI): 1.014-2.057; P<0.05]. The immune microenvironment analysis revealed that the immune score, stromal score, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score were significantly higher in the high-risk group than in the low-risk group. Neutrophils, macrophages, immature dendritic cells (iDCs), natural killer (NK) CD56dim cells, activated DCs (aDCs), and uncharacterized cells were different in the high- and low-risk groups. In addition, the high-risk group had a stronger sensitivity to immunotherapy. Furthermore, the sensitivity of 28 potential chemotherapeutic drugs differed significantly between the high- and low-risk groups.
A novel angiogenesis-related lncRNA signature could be used to predict the prognosis and treatment of GBM.
长链非编码RNA（lncRNA）可调控肿瘤发生、血管生成、增殖及其他肿瘤生物学行为，与胶质瘤的生长和进展密切相关。本研究旨在探讨血管生成相关lncRNA在多形性胶质母细胞瘤（GBM）预后及免疫治疗中的作用。
进行差异分析以获取血管生成相关差异表达lncRNA（AR-DElncRNAs）。然后对AR-DElncRNAs进行单因素Cox分析和最小绝对收缩和选择算子（LASSO）分析以构建预后模型。根据中位风险评分，将患者分为高风险组和低风险组。进行Kaplan-Meier生存分析以评估预后模型的预后价值。此外，构建列线图以通过结合临床病理特征和预后模型预测个体生存概率。此外，进行免疫浸润、免疫治疗和药物敏感性分析以研究高风险组和低风险组之间的差异。
基于单因素Cox分析和LASSO分析，我们从255个AR-DElncRNAs中鉴定出3个与GBM患者生存显著相关的lncRNA（DGCR5、PRKAG2-AS1和ACAP2-IT1）。然后，根据这3个lncRNA构建了一个预后模型，我们发现高风险GBM患者的预后比低风险患者差。此外，风险评分被确定为独立预后因素[风险比（HR）=1.444；95%置信区间（CI）：1.014-2.057；P<0.05]。免疫微环境分析显示，高风险组的免疫评分、基质评分和使用表达数据估计恶性肿瘤组织中的基质和免疫细胞（ESTIMATE）评分显著高于低风险组。中性粒细胞、巨噬细胞、未成熟树突状细胞（iDCs）、自然杀伤（NK）CD56dim细胞、活化树突状细胞（aDCs）和未分类细胞在高风险组和低风险组中存在差异。此外，高风险组对免疫治疗的敏感性更强。此外，28种潜在化疗药物的敏感性在高风险组和低风险组之间存在显著差异。
一种新的血管生成相关lncRNA特征可用于预测GBM的预后和治疗。
