Abstract
BackgroundAs 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.ResultsTo 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.ConclusionsFour 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.
Highlights
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
Cancer development involves many complex and dynamic cellular processes. These processes can be accurately described according to the pathological stages, and the extraction of reliable biomarkers is required to characterize the dynamics of these stages, including (1) stage-specific recurrence somatic copy number alterations (SCNAs), (2) the related aberrant genes, and (3) the enriched dysfunctional pathways [7,8,9,10,11,12]
Most existing network methods for identifying driver genes are based on global networks
Summary
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. Cancer development involves many complex and dynamic cellular processes These processes can be accurately described according to the pathological stages, and the extraction of reliable biomarkers is required to characterize the dynamics of these stages, including (1) stage-specific recurrence somatic copy number alterations (SCNAs), (2) the related aberrant genes, and (3) the enriched dysfunctional pathways [7,8,9,10,11,12]. Cancer is an evolutionary process in which normal cells accumulate various genomic and epigenetic changes, including single-nucleotide variations (SNVs) and chromosomal aberrations. Some of these alterations give mutant cells an advantage in growth and positive selection as well as cause intense proliferation, giving raise to tumors [16]. One of the key questions in cancer genomics is how to distinguish ‘driver’ mutations that cause tumors from ‘passenger’ mutations that are functionally neutral [18]
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