Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma
Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma
复制标题
全面的分析确定了具有强大预测价值的新特征，并揭示了神经胶质瘤的潜在耐药机制
DOI：
10.1186/s12964-019-0492-6
复制
发表时间：
2020-01-06
影响因子：
8.4
通讯作者：
Zhao, Zheng
中科院分区：
文献类型：
--
作者：
Zeng, Fan;Wang, Kuanyu;Zhao, Zheng
BackgroundGliomas are the most common and malignant brain tumors. The standard therapy is surgery combined with radiotherapy, chemotherapy, and/or other comprehensive methods. However, the emergence of chemoresistance is the main obstacle in treatment and its mechanism is still unclear.MethodsWe firstly developed a multi-gene signature by integrated analysis of cancer stem cell and drug resistance related genes. The Chinese Glioma Genome Atlas (CGGA, 325 samples) and The Cancer Genome Atlas (TCGA, 699 samples) datasets were then employed to verify the efficacy of the risk signature and investigate its significance in glioma prognosis. GraphPad Prism, SPSS and R language were used for statistical analysis and graphical work.ResultsThis signature could distinguish the prognosis of patients, and patients with high risk score exhibited short survival time. The Cox regression and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with a well-known chemotherapy impact factor-MGMT promoter methylation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell proliferation, immune response and cell stemness. These mechanisms were confirmed in patient samples.ConclusionsThe signature was an independent and powerful prognostic biomarker in glioma, which would improve risk stratification and provide a more accurate assessment of personalized treatment.Additional file 8 Video abstract
