Genetic polymorphisms associated with pancreatic cancer survival: a genome-wide association study.
Hongwei TangPeng WeiPing ChangYanan LiDong YanChang LiuManal HassanDounghui LiPublished in: International journal of cancer (2017)
Previous findings on the association of genetic factors and pancreatic cancer survival are limited and inconsistent. In a two-stage study, we analyzed the existing genome-wide association study dataset of 868 pancreatic cancer patients from MD Anderson Cancer Center in relation to overall survival using Cox regression. Top hits were selected for replication in another 820 patients from the same institution using the Taqman genotyping method. Functional annotation, pathway analysis and gene expression analysis were conducted using existing software and databases. We discovered genome-wide significant associations of patient survival with three imputed SNPs which, in complete LD (r2 = 1), were intronic SNPs of the PAIP2B (rs113988120) and DYSF genes (rs112493246 and rs138529893) located on Chromosome 2. The variant alleles were associated with a 3.06-fold higher risk of death [95% confidence interval (CI) = 2.10-4.47, p=6.4 × 10-9] after adjusting for clinical factors. Eleven SNPs were tested in the replication study and the association of rs113988120 with survival was confirmed (hazard ratio: 1.57, 95% CI: 1.13-2.20, p=0.008). In silico analysis found rs1139988120 might lead to altered motif. This locus is in LD (D' = 0.77) with three eQTL SNPs near or belong to the NAGK and MCEE genes. According to The Cancer Genome Atlas data and our previous RNA-sequencing data, the mRNA expression level of PAIP2B but not NAGK, MCEE or DYSF was significantly lower in pancreatic tumors than in normal adjacent tissues. Additional validation efforts and functional studies are warranted to demonstrate whether PAIP2B is a novel tumor suppressor gene and a potential therapeutic target for pancreatic cancer.
Keyphrases
- genome wide
- dna methylation
- genome wide association study
- copy number
- free survival
- gene expression
- single cell
- end stage renal disease
- papillary thyroid
- chronic kidney disease
- big data
- genome wide identification
- ejection fraction
- newly diagnosed
- electronic health record
- risk assessment
- deep learning
- rna seq
- high throughput
- prognostic factors
- molecular dynamics
- machine learning
- climate change
- lymph node metastasis
- childhood cancer