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Updated: Dec 27, 2025
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Identification of Novel lncRNA Markers in Glioblastoma Multiforme and Their Clinical Significance: A Study Based on
1Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Background:
Long non-coding RNAs (lncRNAs) have been verified to have a vital role in the progression of glioblastoma multiforme (GBM). Our research was about to identify the potential lncRNAs which was closely associated with the pathogenesis and prognosis of glioblastoma multiforme.
Methods:
All RNA sequence profiling data from patients with GBM were obtained from The Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA). Differently expressed genes identified from GBM and control samples were used to construct competing endogenous RNA (ceRNA) network and perform corresponding functional enrichment analysis. Univariate Cox regression followed by lasso regression and multivariate Cox was used to validate independent lncRNA factors and construct a risk prediction model. Quantitative polymerase chain reaction (qPCR) was performed to verify the expression levels of potential lncRNA biomarkers in human GBM clinical specimens. A gene set enrichment analysis (GSEA) was subsequently conducted to explore potential signaling pathways in which critical lncRNAs may be involved. Moreover, nomogram plot was applied based on our prediction model and significant clinical covariates to visualize the prognosis of GBM patients.
Results:
A total of 2023 differentially expressed genes (DEGs) including 56 lncRNAs, 1587 message RNAs (mRNAs) and 380 other RNAs were included. Based on predictive databases, 16lncRNAs, 32 microRNAs (miRNAs) and 99 mRNAs were used to construct a ceRNA network. Moreover, we performed a novel risk prediction model with 5 potential prognostic lncRNAs, in which 4 of them were newly identified in GBM, to predict the prognosis of GBM patients. Finally, a nomogram plot was constructed to illustrate the potential relationship between the prognosis of GBM and our risk prediction model and significant clinical covariates.
Conclusion:
In this study, we identified 4 novel potential lncRNA biomarkers and constructed a prediction model of GBM prognosis. A simple-to-use nomogram was provided for further clinical application.
