Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing, 401331, China.
Materia Medica Development Group, Institute of Medicinal Chemistry, Lanzhou University School of Pharmacy, Lanzhou, 730000, China.
Sci Rep. 2018 Oct 10;8(1):15104. doi: 10.1038/s41598-018-33323-z.
Glioblastomas (GBMs), are the most common intrinsic brain tumors in adults and are almost universally fatal. Despite the progresses made in surgery, chemotherapy, and radiation over the past decades, the prognosis of patients with GBM remained poor and the average survival time of patients suffering from GBM was still short. Discovering robust gene signatures toward better understanding of the complex molecular mechanisms leading to GBM is an important prerequisite to the identification of novel and more effective therapeutic strategies. Herein, a comprehensive study of genome-scale mRNA expression data by combining GBM and normal tissue samples from 48 studies was performed. The 147 robust gene signatures were identified to be significantly differential expression between GBM and normal samples, among which 100 (68%) genes were reported to be closely associated with GBM in previous publications. Moreover, function annotation analysis based on these 147 robust DEGs showed certain deregulated gene expression programs (e.g., cell cycle, immune response and p53 signaling pathway) were associated with GBM development, and PPI network analysis revealed three novel hub genes (RFC4, ZWINT and TYMS) play important role in GBM development. Furthermore, survival analysis based on the TCGA GBM data demonstrated 38 robust DEGs significantly affect the prognosis of GBM in OS (p < 0.05). These findings provided new insights into molecular mechanisms underlying GBM and suggested the 38 robust DEGs could be potential targets for the diagnosis and treatment.
胶质母细胞瘤（GBM）是成人中最常见的内在脑肿瘤，几乎普遍致命。尽管过去几十年在手术、化疗和放疗方面取得了进展，但 GBM 患者的预后仍然较差，患有 GBM 的患者的平均生存时间仍然很短。发现稳健的基因特征，以更好地了解导致 GBM 的复杂分子机制，是确定新的、更有效的治疗策略的重要前提。在此，通过结合来自 48 项研究的 GBM 和正常组织样本，对全基因组规模的 mRNA 表达数据进行了全面研究。确定了 147 个稳健的基因特征，这些特征在 GBM 和正常样本之间存在显著差异表达，其中 100 个（68%）基因在以前的出版物中与 GBM 密切相关。此外，基于这些 147 个稳健的 DEGs 的功能注释分析表明，某些基因表达程序（例如细胞周期、免疫反应和 p53 信号通路）失调与 GBM 的发展有关，PPI 网络分析显示三个新的枢纽基因（RFC4、ZWINT 和 TYMS）在 GBM 的发展中发挥重要作用。此外，基于 TCGA GBM 数据的生存分析表明，38 个稳健的 DEGs 显著影响 GBM 的 OS 预后（p<0.05）。这些发现为 GBM 的分子机制提供了新的见解，并表明 38 个稳健的 DEGs 可能成为诊断和治疗的潜在靶点。
