4.7 Article

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

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BRITISH JOURNAL OF CANCER
卷 122, 期 4, 页码 569-577

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NATURE PUBLISHING GROUP
DOI: 10.1038/s41416-019-0614-3

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  1. NCI NIH HHS [R35 CA197449, P50 CA127003, R35 CA220523, U01 CA190234, U24 CA231846, P30 CA006516] Funding Source: Medline
  2. NHLBI NIH HHS [P01 HL114501, R01 HL111759, K25 HL140186, P01 HL105339] Funding Source: Medline
  3. NIAID NIH HHS [R01 AI099204] Funding Source: Medline
  4. U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID) [5R01AI099204] Funding Source: Medline
  5. U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) [5P50CA127003, 1R35CA197449] Funding Source: Medline

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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.

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