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Functional annotation of high-quality SNP biomarkers of gastric cancer susceptibility: the Yin Yang of PSCA rs2294008
  1. Hyuna Sung1,
  2. Howard H Yang2,
  3. Nan Hu1,
  4. Hua Su1,
  5. Philip R Taylor1,
  6. Paula L Hyland1
  1. 1 Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, Maryland, USA
  2. 2 Laboratory of Population Genetics, Center for Cancer Research, NCI, NIH, Bethesda, Maryland, USA
  1. Correspondence to Dr Paula L Hyland, Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Institutes of Health, Bethesda, MD 20852, USA; hylandpl{at}mail.nih.gov

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Dear Editor,

We read the recent article by Mocellin et al 1 with great interest. Their comprehensive meta-analysis nominated 11 germline variants at nine loci with high level of cumulative evidence for genetic susceptibility to gastric cancer (GC). Evidence for five of the nine loci (PKLR at 1q22, CASP8 at 2q33.1, MUC1 at 1q22, TGFBR2 at 3p241 and GSTP1 at 11q13.2) came from hypothesis-driven studies based on biological relevance, while the remaining loci were identified through genome-wide association studies (GWAS). However, all loci await functional annotation in the relevant normal tissue to further our understanding of the mechanisms underlying the associations. To explore their functional relevance, we annotated each variant (and its high LD SNPs) using publicly available bioinformatics data in conjunction with our own GWAS and mRNA expression data (figure 1).

Figure 1

Differential effects of rs2294008T on the expression of PSCA in tumour vs normal gastric tissues presumably via interaction with YY1. (A) Expression quantitative trait loci (eQTL) analysis between rs2294008 (risk allele=T) genotypes and PSCA mRNA expression in normal gastric tissues. In the Shanxi study, mRNA levels were assessed using the Affymetrix_U133A probe 205319_at in 89 paired cancer/adjacent normal gastric tissue samples, and correlation coefficients (rho) were estimated using non-parametric Spearman rank correlation tests. From Genotype-Tissue Expression (GTEx), p values were obtained using linear regression between log and quantile normalised RNA-sequencing expression values (of the coding transcript ENSG00000301258 l; …

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