Identification of driver genes based on gene mutational effects and network centrality
Related Experiment Video
Updated: Oct 19, 2025
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Yun-Yun Tang1, Pi-Jing Wei1, Jian-Ping Zhao2
1Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, College of Computer Science and Technology, Anhui University, Hefei, China.
Background:
As one of the deadliest diseases in the world, cancer is driven by a few somatic mutations that disrupt the normal growth of cells, and leads to abnormal proliferation and tumor development. The vast majority of somatic mutations did not affect the occurrence and development of cancer; thus, identifying the mutations responsible for tumor occurrence and development is one of the main targets of current cancer treatments.
Results:
To effectively identify driver genes, we adopted a semi-local centrality measure and gene mutation effect function to assess the effect of gene mutations on changes in gene expression patterns. Firstly, we calculated the mutation score for each gene. Secondly, we identified differentially expressed genes (DEGs) in the cohort by comparing the expression profiles of tumor samples and normal samples, and then constructed a local network for each mutation gene using DEGs and mutant genes according to the protein-protein interaction network. Finally, we calculated the score of each mutant gene according to the objective function. The top-ranking mutant genes were selected as driver genes. We name the proposed method as mutations effect and network centrality.
Conclusions:
Four types of cancer data in The Cancer Genome Atlas were tested. The experimental data proved that our method was superior to the existing network-centric method, as it was able to quickly and easily identify driver genes and rare driver factors.
Insights
Identifying cancer driver genes is crucial for treatment. This study introduces a new method, "mutations effect and network centrality," to pinpoint these key mutations more effectively and efficiently, even rare ones.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Cancer is a major global health threat, driven by specific somatic mutations.
- Identifying these critical driver mutations is essential for developing effective cancer therapies.
- Distinguishing driver mutations from passenger mutations remains a significant challenge.
Purpose of the Study:
- To develop and validate a novel computational method for identifying cancer driver genes.
- To assess the efficacy of the proposed method in pinpointing driver mutations, including rare ones.
Main Methods:
- A semi-local centrality measure and a gene mutation effect function were employed.
- Gene mutation scores were calculated, and differentially expressed genes (DEGs) were identified.
- Protein-protein interaction networks were constructed to analyze gene interactions and calculate driver gene scores.
Main Results:
- The proposed method, "mutations effect and network centrality," successfully identified top-ranking mutant genes as driver genes.
- The method demonstrated superior performance compared to existing network-centric approaches.
- It proved effective in identifying both common and rare driver factors across various cancer types.
Conclusions:
- The "mutations effect and network centrality" method offers a robust and efficient approach for driver gene identification.
- This method can significantly aid in understanding cancer development and guiding targeted treatment strategies.
- The findings highlight the potential of network-based analysis combined with mutation effect assessment in cancer genomics.
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