Yang Biao, Pan Yuan-Bo, Ma Yan-Bin, Chu Sheng-Hua
Department of Neurosurgery, Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Front Oncol. 2020 Mar 17;10:250. doi: 10.3389/fonc.2020.00250. eCollection 2020.
Gliomas are the most prevalent malignant primary brain tumors with poor outcome, and four different molecular subtypes (Mesenchymal, Proneural, Neural, and Classical) are popularly applied in scientific researches and clinics of gliomas. Public databases contain an abundant genome-wide resource to explore the potential biomarker and molecular mechanisms using the informatics analysis. The aim of this study was to discover the potential biomarker and investigate its effect in gliomas. Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression modules and explore the biomarker among the dataset CGGA mRNAseq_693 carrying 693 glioma samples. Functional annotations, ROC, correlation, survival, univariate, and multivariate Cox regression analyses were implemented to investigate the functional effect in gliomas, and molecular experiments were performed to study the biological effect on glioma pathogenesis. The brown module was found to be strongly related to WHO grade of gliomas, and KEGG pathway analysis demonstrated that TNFRSF1A was enriched in MAPK signaling pathway and TNF signaling pathway. Overexpressed TNFRSF1A was strongly related to clinical features such as WHO grade, and functioned as an independent poor prognostic predictor of glioma patients. Notably, TNFRSF1A was preferentially upregulated in the Mesenchymal subtype gliomas (Mesenchymal-associated). Knockdown of TNFRSF1A inhibited proliferation and migration of glioma cell lines . Our findings provide a further understanding of the progression of gliomas, and Mesenchymal-associated TNFRSF1A might be a promising target of diagnosis, therapy, and prognosis of gliomas.
胶质瘤是最常见的原发性恶性脑肿瘤，预后较差，四种不同的分子亚型（间充质型、原神经型、神经型和经典型）广泛应用于胶质瘤的科研和临床。公共数据库包含丰富的全基因组资源，可通过信息学分析探索潜在的生物标志物和分子机制。本研究的目的是发现潜在的生物标志物并研究其在胶质瘤中的作用。使用加权基因共表达网络分析（WGCNA）构建共表达模块，并在包含693个胶质瘤样本的数据集CGGA mRNAseq_693中探索生物标志物。进行功能注释、ROC、相关性、生存、单变量和多变量Cox回归分析以研究其在胶质瘤中的功能作用，并进行分子实验以研究其对胶质瘤发病机制的生物学影响。发现棕色模块与胶质瘤的WHO分级密切相关，KEGG通路分析表明TNFRSF1A在MAPK信号通路和TNF信号通路中富集。TNFRSF1A过表达与WHO分级等临床特征密切相关，是胶质瘤患者独立的不良预后预测指标。值得注意的是，TNFRSF1A在间充质型亚型胶质瘤（间充质相关型）中优先上调。敲低TNFRSF1A可抑制胶质瘤细胞系的增殖和迁移。我们的研究结果进一步加深了对胶质瘤进展的理解，间充质相关的TNFRSF1A可能是胶质瘤诊断、治疗和预后的一个有前景的靶点。
