Xia Pengfei, Huang Yimin, Chen Gang
Department of Neurosurgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
Department of Neurosurgery, Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2022 Aug 10;12:940220. doi: 10.3389/fonc.2022.940220. eCollection 2022.
Necroptosis is closely related to the occurrence and development of tumors, including glioma. A growing number of studies indicate that targeting necroptosis could be an effective treatment strategy against cancer. Long non-coding RNA (lncRNA) is also believed to play a pivotal role in tumor epigenetics. Therefore, it is necessary to identify the functions of necroptosis-related lncRNAs in glioma. In this study, the transcriptome and clinical characteristic data of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases were collected, and the differentially expressed necroptosis-related lncRNAs in TCGA that have an impact on overall survival (OS) were screened out to construct risk score (RS) formula, which was verified in CGGA. A nomogram was constructed to predict the prognosis of glioma patients based on clinical characteristics and RS. In addition, Gene Set Enrichment Analysis (GSEA) was used to analyze the main enrichment functions of these necroptosis-related lncRNAs and the immune microenvironment. A total of nine necroptosis-related lncRNAs have been identified to construct the RS formula, and the Kaplan-Meier (K-M) survival analysis showed significantly poorer outcomes in the high RS group in both TCGA and CGGA databases. Moreover, the receiver operating characteristic (ROC) curve shows that our prediction RS model has good predictability. Regarding the analysis of the immune microenvironment, significant differences were observed in immune function and immune checkpoint between the high RS group and the low RS group. In conclusion, we constructed a necroptosis-related lncRNA RS model that can effectively predict the prognosis of glioma patients and provided the theoretical basis and the potential therapeutic targets for immunotherapy against gliomas.
坏死性凋亡与包括胶质瘤在内的肿瘤的发生和发展密切相关。越来越多的研究表明，靶向坏死性凋亡可能是一种有效的癌症治疗策略。长链非编码RNA（lncRNA）也被认为在肿瘤表观遗传学中起关键作用。因此，有必要确定坏死性凋亡相关lncRNA在胶质瘤中的功能。在本研究中，收集了来自癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）数据库的胶质瘤患者的转录组和临床特征数据，筛选出TCGA中对总生存期（OS）有影响的差异表达的坏死性凋亡相关lncRNA，构建风险评分（RS）公式，并在CGGA中进行验证。基于临床特征和RS构建了列线图以预测胶质瘤患者的预后。此外，基因集富集分析（GSEA）用于分析这些坏死性凋亡相关lncRNA的主要富集功能和免疫微环境。共鉴定出9个坏死性凋亡相关lncRNA以构建RS公式，Kaplan-Meier（K-M）生存分析显示，在TCGA和CGGA数据库中，高RS组的预后均明显较差。此外，受试者工作特征（ROC）曲线表明我们的预测RS模型具有良好的预测性。关于免疫微环境的分析，高RS组和低RS组在免疫功能和免疫检查点方面存在显著差异。总之，我们构建了一个坏死性凋亡相关lncRNA RS模型，该模型可以有效预测胶质瘤患者的预后，并为胶质瘤免疫治疗提供了理论依据和潜在的治疗靶点。
