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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591476/ https://www.ncbi.nlm.nih.gov/pubmed/33110144 http://dx.doi.org/10.1038/s41598-020-75400-2 |
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author | Grassmann, Felix Pawitan, Yudi Czene, Kamila |
author_facet | Grassmann, Felix Pawitan, Yudi Czene, Kamila |
author_sort | Grassmann, Felix |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7591476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75914762020-10-28 A systems genomics approach to uncover the molecular properties of cancer genes Grassmann, Felix Pawitan, Yudi Czene, Kamila Sci Rep Article 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. Nature Publishing Group UK 2020-10-27 /pmc/articles/PMC7591476/ /pubmed/33110144 http://dx.doi.org/10.1038/s41598-020-75400-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Grassmann, Felix Pawitan, Yudi Czene, Kamila A systems genomics approach to uncover the molecular properties of cancer genes |
title | A systems genomics approach to uncover the molecular properties of cancer genes |
title_full | A systems genomics approach to uncover the molecular properties of cancer genes |
title_fullStr | A systems genomics approach to uncover the molecular properties of cancer genes |
title_full_unstemmed | A systems genomics approach to uncover the molecular properties of cancer genes |
title_short | A systems genomics approach to uncover the molecular properties of cancer genes |
title_sort | systems genomics approach to uncover the molecular properties of cancer genes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7591476/ https://www.ncbi.nlm.nih.gov/pubmed/33110144 http://dx.doi.org/10.1038/s41598-020-75400-2 |
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