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Cumulative evidence of relationships between multiple variants in 8q24 region and cancer incidence.

Yu TongYing TangShiping LiFengyan ZhaoJunjie YingYi QuXiaoyu NiuDezhi Mu
Published in: Medicine (2020)
Genome-wide association studies (GWAS) have identified multiple independent cancer susceptibility loci at chromosome 8q24. We aimed to evaluate the associations between variants in the 8q24 region and cancer susceptibility. A comprehensive research synopsis and meta-analysis was performed to evaluate associations between 28 variants in 8q24 and risk of 7 cancers using data from 103 eligible articles totaling 146,932 cancer cases and 219,724 controls. Results: 20 variants were significantly associated with risk of prostate cancer, colorectal cancer, thyroid cancer, breast cancer, bladder cancer, stomach cancer, and glioma, including 1 variant associated with prostate cancer, colorectal cancer, and thyroid cancer. Cumulative epidemiological evidence of an association was graded as strong for DG8S737 -8 allele, rs10090154, rs7000448 in prostate cancer, rs10808556 in colorectal cancer, rs55705857 in gliomas, rs9642880 in bladder cancer, moderate for rs16901979, rs1447295, rs6983267, rs7017300, rs7837688, rs1016343, rs620861, rs10086908 associated in prostate cancer, rs10505477, rs6983267 in colorectal cancer, rs6983267 in thyroid cancer, rs13281615 in breast cancer, and rs1447295 in stomach cancer, weak for rs6983561, rs13254738, rs7008482, rs4242384 in prostate cancer. Data from ENCODE suggested that these variants with strong evidence and other correlated variants might fall within putative functional regions. Our study provides summary evidence that common variants in the 8q24 are associated with risk of multiple cancers in this large-scale research synopsis and meta-analysis. Further studies are needed to explore the mechanisms underlying variants in the 8q24 involved in various human cancers.
Keyphrases
  • prostate cancer
  • copy number
  • papillary thyroid
  • squamous cell carcinoma
  • endothelial cells
  • gene expression
  • squamous cell
  • risk factors
  • young adults
  • deep learning
  • data analysis