A Novel Prognostic Signature of Transcription Factors for the Prediction in Patients With GBM - Cheng, Quan; Huang, Chunhai; Cao, Hui; Lin, Jinhu; Gong, Xuan; Li, Jian; Chen, Yuanbing; Tian, Zhi; Fang, Zhenyu; Huang, Jun (Frontiers in Genetics, 2019)
Background: Although the diagnosis and treatment of glioblastoma (GBM) is significantly improved with recent progresses, there are still 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 transcription factors are identified by data mining with using public databases. GBM transcriptome profile is downloaded from The Cancer Genome Atlas (TCGA). The non-negative matrix factorization (NMF) method is used to cluster the differentially expressed genes to discover hub genes and signal pathways. The transcription factors 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 transcription factors involved potential signaling pathways in GBM are screened by GSEA, GO and KEGG enrichment analysis. Results: There are 68 differentially expressed transcription factors in GBM, of which 43 genes are up-regulated, and 25 genes are down-regulated. NMF clustering analysis suggested that GBM patients are divided into three groups, Cluster A/B/C. LHX2, MEOX2, SNAI2, ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis. The risk score of those 4 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 Curves 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, PI3K-Akt signaling, Hippo signaling, and proteoglycans are highly enriched in high-risk group by GSEA analysis. These genes are mainly involved in cell migration, cell adhesion, 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.