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Updated: Jun 6, 2026
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Integrated analysis of mutations, miRNA and mRNA expression in glioblastoma
Hua Dong1, Li Luo, Shengjun Hong
1State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200433, China.
Background:
Glioblastoma arises from complex interactions between a variety of genetic alterations and environmental perturbations. Little attention has been paid to understanding how genetic variations, altered gene expression and microRNA (miRNA) expression are integrated into networks which act together to alter regulation and finally lead to the emergence of complex phenotypes and glioblastoma.
Results:
We identified association of somatic mutations in 14 genes with glioblastoma, of which 8 genes are newly identified, and association of loss of heterozygosity (LOH) is identified in 11 genes with glioblastoma, of which 9 genes are newly discovered. By gene coexpression network analysis, we identified 15 genes essential to the function of the network, most of which are cancer related genes. We also constructed miRNA coexpression networks and found 19 important miRNAs of which 3 were significantly related to glioblastoma patients' survival. We identified 3,953 predicted miRNA-mRNA pairs, of which 14 were previously verified by experiments in other groups. Using pathway enrichment analysis we also found that the genes in the target network of the top 19 important miRNAs were mainly involved in cancer related signaling pathways, synaptic transmission and nervous systems processes. Finally, we developed new methods to decipher the pathway connecting mutations, expression information and glioblastoma. We identified 4 cis-expression quantitative trait locus (eQTL): TP53, EGFR, NF1 and PIK3C2G; 262 trans eQTL and 26 trans miRNA eQTL for somatic mutation; 2 cis-eQTL: NRAP and EGFR; 409 trans- eQTL and 27 trans- miRNA eQTL for lost of heterozygosity (LOH) mutation.
Conclusions:
Our results demonstrate that integrated analysis of multi-dimensional data has the potential to unravel the mechanism of tumor initiation and progression.
Insights
This study integrates genetic mutations, gene expression, and microRNA networks to understand glioblastoma development. New methods reveal key pathways connecting these factors to glioblastoma initiation and progression.
Area of Science:
- Genomics and Molecular Biology
- Cancer Research
- Neuro-oncology
Background:
- Glioblastoma (GBM) pathogenesis involves complex genetic and environmental interactions.
- Limited understanding exists on how genetic variations, gene expression, and microRNA (miRNA) expression integrate into regulatory networks driving GBM phenotypes.
Purpose of the Study:
- To investigate the integrated network of genetic alterations, gene expression, and miRNA expression in glioblastoma.
- To identify novel genes and miRNAs associated with glioblastoma and patient survival.
- To develop methods for deciphering the pathways connecting molecular alterations to glioblastoma.
Main Methods:
- Somatic mutation and loss of heterozygosity (LOH) analysis.
- Gene and miRNA coexpression network construction.
- Pathway enrichment analysis.
- Development of novel methods for integrated multi-dimensional data analysis, including expression quantitative trait loci (eQTL) analysis.
Main Results:
- Identified associations of somatic mutations in 14 genes (8 novel) and LOH in 11 genes (9 novel) with glioblastoma.
- Constructed gene coexpression networks identifying 15 essential genes and miRNA networks identifying 19 key miRNAs, with 3 significantly impacting glioblastoma patient survival.
- Discovered 3,953 predicted miRNA-mRNA pairs (14 experimentally verified) and identified significant enrichment in cancer-related pathways, synaptic transmission, and nervous system processes.
- Developed novel methods to link mutations and expression data, identifying numerous cis- and trans-eQTLs for both somatic mutations and LOH, including miRNA-eQTLs.
Conclusions:
- Integrated analysis of multi-dimensional data is crucial for unraveling glioblastoma initiation and progression mechanisms.
- The study identified novel genetic and miRNA players and elucidated key molecular pathways involved in glioblastoma.
- The developed methodologies offer a framework for future research into complex cancer systems.
