Luo Na, Sun Xizi, Ma Shengling, Li Xiaoyu, Zhu Wenjun, Fu Min, Yang Feng, Chen Ziqi, Li Qianxia, Zhang Yuanyuan, Peng Xiaohong, Hu Guangyuan
Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Oncol. 2022 Jul 20;12:868415. doi: 10.3389/fonc.2022.868415. eCollection 2022.
Accumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified.
We obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX-least absolute shrinkage and selection operator (LASSO)-multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection assays to determine the biological role of CD81 in glioblastoma cells.
Our glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients' survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells.
We developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.
越来越多的证据表明，m6A可调节癌基因和肿瘤抑制基因的表达，从而在癌症中发挥双重作用。同样，免疫系统与肿瘤的发生发展密切相关。然而，对于胶质母细胞瘤，与m6A相关的免疫标志物仍有待确定。
我们从癌症基因组图谱（The Cancer Genome Atlas）和中国胶质瘤基因组图谱（Chinese Glioma Genome Atlas）数据库中获取了胶质母细胞瘤的基因表达、突变和临床数据。接下来，我们进行单变量COX-最小绝对收缩和选择算子（LASSO）-多变量COX回归分析，以建立一个预后基因特征并开发相应的动态列线图应用程序。然后，我们进行了两次聚类分析，根据所有样本的m6A调节基因和m6A相关免疫基因表达水平（高、中、低）对其进行分类，并计算它们的m6A评分。最后，我们进行了定量逆转录-聚合酶链反应、细胞计数试剂盒-8、细胞干性检测、细胞迁移和凋亡检测实验，以确定CD81在胶质母细胞瘤细胞中的生物学作用。
我们的胶质母细胞瘤风险评分模型具有极高的预测效能，受试者工作特征曲线下面积达到0.9。动态列线图应用程序的网络版允许快速准确地计算患者的生存几率。生存曲线和桑基图表明，高m6A评分组对应于m6A调节基因水平中等和低且m6A相关预后免疫基因水平高的组。此外，这些组的生存率较低，免疫浸润较高。基于基因集富集分析，病理生理机制可能与免疫抑制功能和相关信号通路的激活有关。此外，风险评分模型使我们能够进行免疫治疗获益评估。最后，沉默CD81可显著抑制胶质母细胞瘤细胞的增殖、干性和迁移，并促进其凋亡。
我们开发了一个准确有效的预后模型。此外，不同分层方法与肿瘤微环境的相关性分析为进一步探索病理生理机制提供了依据。最后，CD81可能作为胶质母细胞瘤的诊断和预后生物标志物。
