A functional variant in the boundary of a topological association domain is associated with pancreatic cancer risk.
Shufang MeiJuntao KeJianbo TianPingting YingNan YangXiaoyang WangDanyi ZouXiating PengYang YangYing ZhuYajie GongRong ZhongJiang ChangXiaoping MiaoPublished in: Molecular carcinogenesis (2019)
As the proper binding of CCCTC-binding factor (CTCF) in the boundaries of topological association domains (TADs) was important for chromatin structures and gene regulation, we hypothesized that single nucleotide polymorphisms (SNPs) affecting CTCF binding in TAD boundaries might contribute to pancreatic cancer (PC) susceptibility. We first genome widely screened out potential SNPs via bioinformatics analysis on Hi-C data, ChIP-seq data, and CTCF binding motif, then tested their associations with PC risk in a previous genome-wide association studies (GWASs) data set (981 cases and 1,991 controls), followed by another independent replication set (1,208 cases and 1,465 controls). Electrophoretic mobility shift assays (EMSAs), expression Quantitative Trait Loci (eQTL) analyses and cell proliferation experiments were performed to uncover the biological mechanisms. The positive SNP rs2001389 was found significantly associated with PC risk with odds ratio (OR) being 1.166 (95% confidence interval (CI) = 1.075-1.264, P = 2.143E-04) in the combined study. The allele G of rs2001389 weakened the binding activity with CTCF, and it was related to the lower expression of a putative antioncogene MFSD13A whose knockdown promoted proliferation of PC cells. By integrating analysis on multiomics data, association studies and functional assays, we proposed that the common variant rs2001389 and the gene MFSD13A might be genetic modifiers of PC tumorigenesis.
Keyphrases
- genome wide
- genome wide association
- dna methylation
- binding protein
- electronic health record
- copy number
- cell proliferation
- poor prognosis
- big data
- high throughput
- dna binding
- gene expression
- bioinformatics analysis
- dna damage
- signaling pathway
- transcription factor
- cell cycle
- data analysis
- risk assessment
- machine learning
- mass spectrometry
- artificial intelligence
- high density