A Novel Prognostic Signature of Transcription Factors for the Prediction in Patients With GBM
A Novel Prognostic Signature of Transcription Factors for the Prediction in Patients With GBM
复制标题
用于预测 GBM 患者的转录因子的新预后特征
DOI：
10.3389/fgene.2019.00906
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发表时间：
2019-10-01
影响因子：
3.7
通讯作者：
Huang, Jun
中科院分区：
文献类型：
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作者：
Cheng, Quan;Huang, Chunhai;Huang, Jun
背景资料：尽管胶质母细胞瘤（GBM）的诊断和治疗随着近年来的进展而显著提高，但在治疗效果和总生存期方面仍然存在很大的异质性。本研究的目的是分析GBM中转录因子的基因表达，以发现新的肿瘤标志物。方法：利用公共数据库进行数据挖掘，找出差异表达的转录因子。GBM转录组谱从癌症基因组图谱（TCGA）下载。采用非负矩阵因子分解（NMF）方法对差异表达基因进行聚类，发现枢纽基因和信号通路。采用单因素和多因素考克斯回归分析筛选影响GBM预后的TF，并绘制受试者工作特征（ROC）曲线。通过整合临床数据，构建GBM风险模型和诺模图。最后，通过基因集富集分析（Gene Set Enrichment Analysis，GSEA）、基因本体论（Gene Ontology，GO）和京都基因与基因组百科全书（Encyclopedia of Genes and Genomes，KEGG）富集分析，筛选出68个与GBM中潜在信号通路相关的转录因子。NMF聚类分析表明，GBM患者分为三组：A，B和C。通过单变量/多变量回归分析从上述差异基因中鉴定出LHX 2、MEOX 2、SNAI 2和ZNF 22。根据每个基因的β系数计算这四个基因的风险评分，通过时间依赖性ROC曲线分析发现，随着预测终止时间的延长，风险评分的预测能力逐渐增强。诺模图结果显示，风险评分、年龄、性别、化疗、放疗和1 p/19 q的整合可以进一步提高对GBM生存的预测能力。癌症中的途径，磷酸肌醇3-激酶（PI 3 K）-Akt信号传导，Hippo信号传导和蛋白聚糖，通过GSEA在高危人群中高度富集。结论：LHX 2、MEOX 2、SNAI 2和ZNF 22四因子联合评分模型可准确预测GBM患者的预后。
Background: Although the diagnosis and treatment of glioblastoma (GBM) is significantly improved with recent progresses, there is still a large heterogeneity in therapeutic effects and overall survival. The aim of this study is to analyze gene expressions of transcription factors (TFs) in GBM so as to discover new tumor markers.Methods: Differentially expressed TFs are identified by data mining using public databases. The GBM transcriptome profile is downloaded from The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) method is used to cluster the differentially expressed genes to discover hub genes and signal pathways. The TFs affecting the prognosis of GBM are screened by univariate and multivariate COX regression analysis, and the receiver operating characteristic (ROC) curve is determined. The GBM hazard model and nomogram map are constructed by integrating the clinical data. Finally, the TFs involving potential signaling pathways in GBM are screened by Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.Results: There are 68 differentially expressed TFs in GBM, of which 43 genes are upregulated and 25 genes are downregulated. NMF clustering analysis suggested that GBM patients are divided into three groups: Clusters A, B, and C. LHX2, MEOX2, SNAI2, and ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis. The risk score of those four genes are calculated based on the beta coefficient of each gene, and we found that the predictive ability of the risk score gradually increased with the prolonged predicted termination time by time-dependent ROC curve analysis. The nomogram results have showed that the integration of risk score, age, gender, chemotherapy, radiotherapy, and 1p/19q can further improve predictive ability towards the survival of GBM. The pathways in cancer, phosphoinositide 3-kinases (PI3K)-Akt signaling, Hippo signaling, and proteoglycans, are highly enriched in high-risk groups by GSEA. These genes are mainly involved in cell migration, cell adhesion, epithelial-mesenchymal transition (EMT), cell cycle, and other signaling pathways by GO and KEGG analysis.Conclusion: The four-factor combined scoring model of LHX2, MEOX2, SNAI2, and ZNF22 can precisely predict the prognosis of patients with GBM.