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Nongenic cancer-risk SNPs affect oncogenes, tumour-suppressor genes, and immune function

Authors :
John Platig
Maud Fagny
John Quackenbush
Marieke L. Kuijjer
Xihong Lin
Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon)
AgroParisTech-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Channing Division of Network Medicine
Brigham and Women's Hospital [Boston]
Harvard Medical School [Boston] (HMS)
Department of Biostatistics and Computational Biology
Dana-Farber Cancer Institute [Boston]
Department of Biostatistics
Harvard T.H. Chan School of Public Health
Centre for Molecular Medicine Norway
University of Oslo (UiO)
Department of Cancer Biology
United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Cancer Institute (NCI)R35 CA197449R35 CA220523United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Heart Lung & Blood Institute (NHLBI)K25 HL140186U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) 1R35CA1974495P50CA127003U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) 5R01AI099204
Source :
British Journal of Cancer, British Journal of Cancer, Cancer Research UK, 2020, 122 (4), pp.569-577. ⟨10.1038/s41416-019-0614-3⟩
Publication Year :
2020
Publisher :
HAL CCSD, 2020.

Abstract

Background Genome-wide association studies (GWASes) have identified many noncoding germline single-nucleotide polymorphisms (SNPs) that are associated with an increased risk of developing cancer. However, how these SNPs affect cancer risk is still largely unknown. Methods We used a systems biology approach to analyse the regulatory role of cancer-risk SNPs in thirteen tissues. By using data from the Genotype-Tissue Expression (GTEx) project, we performed an expression quantitative trait locus (eQTL) analysis. We represented both significant cis- and trans-eQTLs as edges in tissue-specific eQTL bipartite networks. Results Each tissue-specific eQTL network is organised into communities that group sets of SNPs and functionally related genes. When mapping cancer-risk SNPs to these networks, we find that in each tissue, these SNPs are significantly overrepresented in communities enriched for immune response processes, as well as tissue-specific functions. Moreover, cancer-risk SNPs are more likely to be ‘cores’ of their communities, influencing the expression of many genes within the same biological processes. Finally, cancer-risk SNPs preferentially target oncogenes and tumour-suppressor genes, suggesting that they may alter the expression of these key cancer genes. Conclusions This approach provides a new way of understanding genetic effects on cancer risk and provides a biological context for interpreting the results of GWAS cancer studies.

Details

Language :
English
ISSN :
00070920 and 15321827
Database :
OpenAIRE
Journal :
British Journal of Cancer, British Journal of Cancer, Cancer Research UK, 2020, 122 (4), pp.569-577. ⟨10.1038/s41416-019-0614-3⟩
Accession number :
edsair.doi.dedup.....cd02fa1eafd0310864234a8750a203fd
Full Text :
https://doi.org/10.1038/s41416-019-0614-3⟩