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Updated: May 14, 2026
Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
Published on: October 4, 2019
Network analysis of genomic alteration profiles reveals co-altered functional modules and driver genes for
Yunyan Gu1, Hongwei Wang, Yao Qin
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China. guyunyan@ems.hrbmu.edu.cn
Abstract:
The heterogeneity of genetic alterations in human cancer genomes presents a major challenge to advancing our understanding of cancer mechanisms and identifying cancer driver genes. To tackle this heterogeneity problem, many approaches have been proposed to investigate genetic alterations and predict driver genes at the individual pathway level. However, most of these approaches ignore the correlation of alteration events between pathways and miss many genes with rare alterations collectively contributing to carcinogenesis. Here, we devise a network-based approach to capture the cooperative functional modules hidden in genome-wide somatic mutation and copy number alteration profiles of glioblastoma (GBM) from The Cancer Genome Atlas (TCGA), where a module is a set of altered genes with dense interactions in the protein interaction network. We identify 7 pairs of significantly co-altered modules that involve the main pathways known to be altered in GBM (TP53, RB and RTK signaling pathways) and highlight the striking co-occurring alterations among these GBM pathways. By taking into account the non-random correlation of gene alterations, the property of co-alteration could distinguish oncogenic modules that contain driver genes involved in the progression of GBM. The collaboration among cancer pathways suggests that the redundant models and aggravating models could shed new light on the potential mechanisms during carcinogenesis and provide new indications for the design of cancer therapeutic strategies.
Insights
This study introduces a network-based method to uncover cooperative gene modules in glioblastoma (GBM) by analyzing genetic alterations. It reveals significant co-altered pathways, offering new insights into cancer mechanisms and therapeutic strategies.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Genetic alterations in human cancer genomes are highly heterogeneous, complicating cancer mechanism understanding and driver gene identification.
- Existing pathway-level approaches often overlook inter-pathway correlations and genes with rare, collective contributions to carcinogenesis.
Purpose of the Study:
- To develop a network-based approach for identifying cooperative functional modules in glioblastoma (GBM) by analyzing genome-wide genetic alterations.
- To capture the non-random correlations between gene alterations across pathways.
Main Methods:
- Utilized genome-wide somatic mutation and copy number alteration data from The Cancer Genome Atlas (TCGA) for glioblastoma.
- Employed a network-based approach to define and identify cooperative functional modules based on dense interactions within protein-protein interaction networks.
- Analyzed co-alteration patterns among identified modules.
Main Results:
- Identified 7 pairs of significantly co-altered modules in GBM, involving key signaling pathways like TP53, RB, and RTK.
- Highlighted striking co-occurring alterations within these GBM pathways.
- Demonstrated that co-alteration properties can distinguish oncogenic modules containing GBM driver genes.
Conclusions:
- The collaboration among cancer pathways provides a new perspective on carcinogenesis mechanisms, including redundant and aggravating models.
- Findings offer novel indications for designing targeted cancer therapeutic strategies for glioblastoma.
