Li Xiaomeng, Sun Li, Wang Xue, Wang Nan, Xu Kanghong, Jiang Xinquan, Xu Shuo
School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, China.
Department of Neurosurgery, Qilu Hospital of Shandong University, Jinan, China.
Front Mol Biosci. 2021 Feb 16;8:632837. doi: 10.3389/fmolb.2021.632837. eCollection 2021.
A variety of regulatory approaches including immune modulation have been explored as approaches to either eradicate antitumor response or induce suppressive mechanism in the glioblastoma microenvironment. Thus, the study of immune-related long noncoding RNA (lncRNA) signature is of great value in the diagnosis, treatment, and prognosis of glioblastoma. Glioblastoma samples with lncRNA sequencing and corresponding clinical data were acquired from the Cancer Genome Atlas (TCGA) database. Immune-lncRNAs co-expression networks were built to identify immune-related lncRNAs via Pearson correlation. Based on the median risk score acquired in the training set, we divided the samples into high- and low-risk groups and demonstrate the survival prediction ability of the immune-related lncRNA signature. Both principal component analysis (PCA) and gene set enrichment analysis (GSEA) were used for immune state analysis. A cohort of 151 glioblastoma samples and 730 immune-related genes were acquired in this study. A five immune-related lncRNA signature (, and ) was identified. Compared with patients in the high-risk group, patients in the low-risk group showed a longer overall survival (OS) in the training, validation, and entire TCGA set ( = 1.931e-05, = 1.706e-02, and = 3.397e-06, respectively). Additionally, the survival prediction ability of this lncRNA signature was independent of known clinical factors and molecular features. The area under the ROC curve (AUC) and stratified analyses were further performed to verify its optimal survival predictive potency. Of note, the high-and low-risk groups exhibited significantly distinct immune state according to the PCA and GSEA analyses. Our study proposes that a five immune-related lncRNA signature can be utilized as a latent indicator of prognosis and potential therapeutic approach for glioblastoma.
包括免疫调节在内的多种调控方法已被探索作为根除抗肿瘤反应或诱导胶质母细胞瘤微环境中抑制机制的方法。因此，免疫相关长链非编码RNA（lncRNA）特征的研究在胶质母细胞瘤的诊断、治疗和预后方面具有重要价值。从癌症基因组图谱（TCGA）数据库中获取了具有lncRNA测序和相应临床数据的胶质母细胞瘤样本。构建免疫-lncRNAs共表达网络，通过Pearson相关性识别免疫相关lncRNAs。基于在训练集中获得的中位风险评分，我们将样本分为高风险组和低风险组，并证明了免疫相关lncRNA特征的生存预测能力。主成分分析（PCA）和基因集富集分析（GSEA）均用于免疫状态分析。本研究获得了151个胶质母细胞瘤样本和730个免疫相关基因。鉴定出了一个由五个免疫相关lncRNA组成的特征（、和）。与高风险组患者相比，低风险组患者在训练集、验证集和整个TCGA数据集中的总生存期（OS）更长（分别为=1.931e-05、=1.706e-02和=3.397e-06）。此外，这种lncRNA特征的生存预测能力独立于已知的临床因素和分子特征。进一步进行ROC曲线下面积（AUC）和分层分析以验证其最佳生存预测效力。值得注意的是，根据PCA和GSEA分析，高风险组和低风险组表现出明显不同的免疫状态。我们的研究表明，一个由五个免疫相关lncRNA组成的特征可作为胶质母细胞瘤预后的潜在指标和潜在治疗方法。
