A systems genomics approach to uncover the molecular properties of cancer genes

Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped accor...

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Bibliographic Details
Published in:Scientific reports 2020-10, Vol.10 (1), p.18392-18392, Article 18392
Main Authors: Grassmann, Felix, Pawitan, Yudi, Czene, Kamila
Format: Article
Language:English
Subjects:
631/114/2401
631/114/2785
631/208/182
631/208/191
631/208/199
631/208/69
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Algorithms
Binding sites
Breast cancer
Cancer
Exons
Genes
Genetic Predisposition to Disease
Genome-wide association studies
Genome-Wide Association Study
Genomics
Genomics - methods
Humanities and Social Sciences
Humans
Isoforms
Linkage Disequilibrium
multidisciplinary
Mutation
Neoplasms - genetics
Proof of Concept Study
Science
Science (multidisciplinary)
Survival
Systems Biology
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Summary:Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-75400-2