Luo Qisheng, Yang Zhenxiu, Deng Renzhi, Pang Xianhui, Han Xu, Liu Xinfu, Du Jiahai, Tian Yingzhao, Wu Jingzhan, Tang Chunhai
Department of Neurosurgery, Affiliated Hospital of Youjiang Medical University for Nationalities, BaiSe,Guangxi province, 533000, China.
Department of Oncology, The Second Affiliated Hospital of Guangxi Medical University,NanNing, Guangxi province,530000, China.
Heliyon. 2023 Jan 5;9(2):e12838. doi: 10.1016/j.heliyon.2023.e12838. eCollection 2023 Feb.
To investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients' prognosis.
The Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We performed Pearson correlation, univariate Cox regression to screen m6A-related prognostic lncRNA. GMB patients' samples were separated into different clusters through the ConsensusClusterPlus package. The risk score model was established through LASSO regression analysis. Besides, KEGG pathway enrichment analysis was implemented. CIBERSORT algorithm was used to analyze the difference of 22 types of immune cell infiltration in different cluster of GBM patient. Cox regression analyses were used to verify the independence of the model and correlation analysis was performed to demonstrate the link between our model and clinical characteristics of GBM patients. Experiments were used to validate the differential expression of the model lncRNA in patients with different prognosis.
17 lncRNA related to prognosis were screened from 1021 m6A-related lncRNAs. Further, four m6A-related lncRNAs that were significantly correlated with GBM prognosis were selected to establish our prognostic risk model, which had excellent accuracy and can independently predict the prognosis of GBM patients. The infiltration fractions of T regulatory cells, T cells CD4 memory activated and neutrophils were positively associated with risk score, which suggested a significant relationship between the model and tumor immune microenvironment.
The m6A-related RNA risk model offered potential for identifying biomarkers of therapy and predicting prognosis of GBM patients.
研究多形性胶质母细胞瘤（GBM）中的免疫细胞浸润状态，并构建一种能够预测患者预后的新型预后风险模型。
利用癌症基因组图谱（TCGA）数据库获取RNA序列信息和相关临床数据。我们进行Pearson相关性分析、单变量Cox回归以筛选与m6A相关的预后lncRNA。通过ConsensusClusterPlus软件包将GBM患者样本分为不同簇。通过LASSO回归分析建立风险评分模型。此外，实施KEGG通路富集分析。使用CIBERSORT算法分析GBM患者不同簇中22种免疫细胞浸润的差异。采用Cox回归分析验证模型的独立性，并进行相关性分析以证明我们的模型与GBM患者临床特征之间的联系。通过实验验证模型lncRNA在不同预后患者中的差异表达。
从1021个与m6A相关的lncRNA中筛选出17个与预后相关的lncRNA。进一步地，选择4个与GBM预后显著相关的m6A相关lncRNA建立我们的预后风险模型，该模型具有良好的准确性，能够独立预测GBM患者的预后。调节性T细胞、CD4记忆性活化T细胞和中性粒细胞的浸润分数与风险评分呈正相关，这表明该模型与肿瘤免疫微环境之间存在显著关系。
与m6A相关的RNA风险模型为识别治疗生物标志物和预测GBM患者预后提供了潜力。
