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Updated: Oct 4, 2025
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Identification of potential microRNAs in glioblastoma using bioinformatic analysis and prognostic evaluation
Zhenwei Sun1, Yongquan Zhao2, Xuan Ding1
1Department of Neurosurgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Background:
Glioblastoma (GB) is the most common and aggressive brain and central nervous system malignancy. MicroRNAs (miRNAs) have been demonstrated to be predictors of prognostic outcomes, playing an important role in the pathogenesis and progression of GB. We aim to identify the potential miRNAs in GB.
Methods:
GSE103228 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed miRNAs (DE-miRNAs) using the Student's t-test. Potential target genes for DE-miRNAs were predicted using miRTarBase, and their functions were analyzed using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The protein-protein interaction (PPI) network was constructed using the STRING database and visualized using Cytoscape to identify a hub target gene-miRNA network. Furthermore, the expression of GB target genes was verified using University of Alabama Cancer (UALCAN) database.
Results:
A total of 49 DE-miRNAs were identified in GB including 30 down-regulated miRNAs and 19 up-regulated miRNAs. Our analysis predicted 1,118 and 1,063 potential target genes from the top three most up-regulated and down-regulated DE-miRNAs, respectively, that were enriched in several GB-related pathways including the cancer pathway. ACTB and MYC were considered to be hub genes in our PPI networks.
Conclusions:
MiR-218-5p and miR-148a-3p regulated most of the hub genes and miR-148a-3p appeared to be a prognostic biomarker.
