Chen Di, Xu Yuan, Gao Xueping, Zhu Xuqiang, Liu Xianzhi, Yan Dongming
Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
School of Basic Medicine, Gannan Medical University, Ganzhou, Jiangxi, China.
Front Pharmacol. 2023 Apr 10;14:1158723. doi: 10.3389/fphar.2023.1158723. eCollection 2023.
Glioma patients often experience unfavorable outcomes and elevated mortality rates. Our study established a prognostic signature utilizing cuproptosis-associated long non-coding RNAs (CRLs) and identified novel prognostic biomarkers and therapeutic targets for glioma. The expression profiles and related data of glioma patients were obtained from The Cancer Genome Atlas, an accessible online database. We then constructed a prognostic signature using CRLs and evaluated the prognosis of glioma patients by means of Kaplan-Meier survival curves and receiver operating characteristic curves. A nomogram based on clinical features was employed to predict the individual survival probability of glioma patients. Functional enrichment analysis was conducted to identify crucial CRL-related enriched biological pathways. The role of LEF1-AS1 in glioma was validated in two glioma cell lines (T98 and U251). We developed and validated a prognostic model for glioma with 9 CRLs. Patients with low-risk had a considerably longer overall survival (OS). The prognostic CRL signature may serve independently as an indicator of prognosis for glioma patients. In addition, functional enrichment analysis revealed significant enrichment of multiple immunological pathways. Notable differences were observed between the two risk groups in terms of immune cell infiltration, function, and immune checkpoints. We further identified four drugs based on their different IC50 values from the two risk groups. Subsequently, we discovered two molecular subtypes of glioma (cluster one and cluster two), with the cluster one subtype exhibiting a remarkably longer OS compared to the cluster two subtype. Finally, we observed that inhibition of LEF1-AS1 curbed the proliferation, migration, and invasion of glioma cells. The CRL signatures were confirmed as a reliable prognostic and therapy response indicator for glioma patients. Inhibition of LEF1-AS1 effectively suppressed the growth, migration, and invasion of gliomas; therefore, LEF1-AS1 presents itself as a promising prognostic biomarker and potential therapeutic target for glioma.
胶质瘤患者通常预后不良且死亡率较高。我们的研究利用铜死亡相关长链非编码RNA(CRLs)建立了一种预后特征,并确定了胶质瘤新的预后生物标志物和治疗靶点。胶质瘤患者的表达谱及相关数据来自可访问的在线数据库癌症基因组图谱(The Cancer Genome Atlas)。然后,我们使用CRLs构建了一种预后特征,并通过Kaplan-Meier生存曲线和受试者工作特征曲线评估胶质瘤患者的预后。采用基于临床特征的列线图来预测胶质瘤患者的个体生存概率。进行功能富集分析以确定关键的CRL相关富集生物途径。在两种胶质瘤细胞系(T98和U251)中验证了LEF1-AS1在胶质瘤中的作用。我们开发并验证了一种包含9个CRL的胶质瘤预后模型。低风险患者的总生存期(OS)明显更长。预后CRL特征可独立作为胶质瘤患者预后的指标。此外,功能富集分析显示多种免疫途径显著富集。在免疫细胞浸润、功能和免疫检查点方面,两个风险组之间观察到显著差异。我们根据两个风险组不同的半数抑制浓度(IC50)值进一步确定了四种药物。随后,我们发现了胶质瘤的两种分子亚型(簇一和簇二),与簇二亚型相比,簇一亚型的OS明显更长。最后,我们观察到抑制LEF1-AS1可抑制胶质瘤细胞的增殖、迁移和侵袭。CRL特征被确认为胶质瘤患者可靠的预后和治疗反应指标。抑制LEF1-AS1可有效抑制胶质瘤的生长、迁移和侵袭;因此,LEF1-AS1是一种有前景的胶质瘤预后生物标志物和潜在治疗靶点。