Establishment of LncRNA Risk Prediction Model for Glioblastoma Based on TCGA Database

Abstract

Objective To establish a risk score model of LncRNA for the prognosis of glioblastoma patients using TCGA database. Methods The gene expression profiles and clinical data of glioblastoma and normal nerve tissues in TCGA database were downloaded to screen differentially-expressed LncRNA. The risk score model of LncRNA was screened and established by univariate and multivariate Cox regression models. Results The expression profiles of glioblastoma genes were obtained from TCGA database, including 169 glioblastoma tissues and 5 normal nerve tissues. The R software edgeR package was used for differentially- expressed gene analysis (logFC≥2 or ≤-2, FDR < 0.05, FDR < 0.05). A total of 7978 differential expressed genes were obtained, of which 1643 were differential expressed lncRNAs. By univariate and multivariate Cox regression analyses, the prognostic risk model was obtained: Risk score=0.59×NDUFB2-AS1-0.41×ZEB1-AS1+0.31×AL139385.1+0.21×AGAP2-AS1. The area under ROC curve(AUC) of the model was 0.864. Risk scores results indicated that the prognosis of patients with high score was worse than that of patients with low score. Conclusion The risk prediction models of NDUFB2-AS1, ZEB1-AS1, AL139385.1 and AGAP2-AS1 mentioned above could effectively predict the prognosis of glioblastoma patients and are expected to be used for clinical treatment guidance.

Keywords

• glioblastoma
• lncrna
• tcga database
• cox regression model