| Literature DB >> 26302849 |
Qinghua Su1, Yuan Wang2, Jun Zhao3, Cangjian Ma4, Tao Wu5, Tianbo Jin6,7, Jinkai Xu8.
Abstract
BACKGROUND: Gastric and colorectal cancers have a major impact on public health, and are the most common malignant tumors in China. The aim of this research was to study whether polymorphisms of CHCHD3P1-HSP90AB7P, GRID1, HSPA12A, PRLHR, SBF2, POLD3 and C11orf93-C11orf92 genes are associated with the risk of gastric and colorectal cancers in the Chinese Han population.Entities:
Mesh:
Substances:
Year: 2015 PMID: 26302849 PMCID: PMC4548694 DOI: 10.1186/s12876-015-0336-9
Source DB: PubMed Journal: BMC Gastroenterol ISSN: 1471-230X Impact factor: 3.067
Demographic characteristics of patients with gastric and colorectal cancers, and controls
| Group (N) | Age (years) | Gender (male/female) | ||
|---|---|---|---|---|
| Healthy controls ( | 48.57 ± 9.43 | 396/307 | – | – |
| Gastric cancer cases ( | 58.12 ± 11.66 | 392/196 | 0.21 | 0.54 |
| Colorectal cancer cases ( | 59.09 ± 11.78 | 260/189 | 0.32 | 0.25 |
aP value is based on the age versus healthy controls in the study
bP value is based on the gender versus healthy controls in the study
Primers used for this study
| SNP ID | 1st – PCR primer sequences | 2nd – PCR primer sequences | UEP sequences |
|---|---|---|---|
| rs10795668 | ACGTTGGATGAATACTTGTACCTTGGTGGG | ACGTTGGATGTCATCTATGAGCAGCAGCAG | gcGAAAGAGAAAAAGTTAGATTCTTA |
| rs10788473 | ACGTTGGATGCAGGAAGTGACAGCTATCTC | ACGTTGGATGGGCTTCATTGGGAGCTAGTG | ggggaTCCAAGCTACGGCTCACCTGG |
| rs1665650 | ACGTTGGATGCCAACTGAGGATGATTTGAC | ACGTTGGATGGGTTGTTTGGCTACTCAAAG | ctccAAATGTCTATCGCCTTTAC |
| rs12413624 | ACGTTGGATGGCTAGGTGTGGCACTGTTTG | ACGTTGGATGTTATGCAACTGGTCCTGGTC | tgggtTGGTCCTGGTCAGATGTTAT |
| rs10500715 | ACGTTGGATGAGGCTTGAGATTTGGAAGGC | ACGTTGGATGCCATCTTTAGATCTTCTCTC | cttTTTAGATCTTCTCTCAGTCTA |
| rs3824999 | ACGTTGGATGCTAAATCCCCTTTGCTGGAC | ACGTTGGATGGATCAGAGAACTACAAGCAC | TTCTCCATTGGTTCTCTAA |
| rs3802842 | ACGTTGGATGCATCGTTTTGTTAGGAAGAC | ACGTTGGATGGGCCCCTAAAATGAGGTGAA | aagGAGGTGAATTTCTGGGA |
PCR polymerase chain reaction, UEP unextended mini-sequencing primer
Basic information of candidate SNPs in this study
| SNP ID | Gene | HWE | Alleles A/B | MAF control | MAF case | |
|---|---|---|---|---|---|---|
| Gastric cancer | Colorectal cancer | |||||
| rs10795668 |
| 0.1739 | A/G | 0.384 | 0.369 | 0.348 |
| rs10788473 |
| 0.7497 | T/C | 0.383 | 0.391 | 0.378 |
| rs1665650 |
| 1 | A/G | 0.312 | 0.316 | 0.33 |
| rs12413624 |
| 0.3968 | A/T | 0.431 | 0.405 | 0.381 |
| rs10500715 |
| 0.9056 | G/T | 0.198 | 0.212 | 0.205 |
| rs3824999 |
| 0.7421 | C/A | 0.361 | 0.346 | 0.391 |
| rs3802842 |
| 0.3986 | C/A | 0.435 | 0.441 | 0.478 |
A/B stands for minor/major alleles on the control sample frequencies
SNPs are excluded at 5 % HWE P level
Association of SNPs with risk of gastric and colorectal cancers based on logistic tests adjusted by gender and age
| SNP ID | Model | Genotype | Gastric cancer | Colorectal cancer | ||||
|---|---|---|---|---|---|---|---|---|
| OR (95 % CI) | OR (95 % CI) | |||||||
| rs10795668 | Allele model | A/G | 0.94 | (0.80–1.10) | 0.419 | 0.86 | (0.70–1.02) | 0.082 |
| Overdominant model | A/G | 0.97 | (0.76–1.23) | 0.78 | 0.89 | (0.68–1.17) | 0.41 | |
| Log - additive model | – | 0.95 | (0.80–1.13) | 0.56 | 0.86 | (0.71–1.04) | 0.12 | |
| rs10788473 | Allele model | T/C | 1.04 | (0.88–1.22) | 0.655 | 0.98 | (0.82–1.17) | 0.817 |
| Overdominant model | T/C | 0.86 | (0.68–1.10) | 0.24 | 1.06 | (0.81–1.39) | 0.66 | |
| Log - additive model | – | 1.04 | (0.88–1.24) | 0.63 | 0.91 | (0.75–1.11) | 0.34 | |
| rs1665650 | Allele model | A/G | 1.02 | (0.86–1.21) | 0.85 | 1.09 | (0.91–1.30) | 0.363 |
| Overdominant model | A/G | 0.77 | (0.60–0.99) | 0.038* | 1.04 | (0.80–1.36) | 0.78 | |
| Log - additive model | – | 1 | (0.83–1.20) | 0.99 | 1.16 | (0.95–1.42) | 0.15 | |
| rs12413624 | Allele model | A/T | 0.9 | (0.77–1.05) | 0.191 | 0.81 | (0.68–0.96) | 0.018* |
| Overdominant model | A/T | 0.91 | (0.72–1.17) | 0.47 | 0.9 | (0.69–1.18) | 0.44 | |
| Log - additive model | – | 0.93 | (0.77–1.11) | 0.39 | 0.81 | (0.66–0.98) | 0.032* | |
| rs10500715 | Allele model | G/T | 1.09 | (0.90–1.32) | 0.387 | 1.04 | (0.85–1.28) | 0.699 |
| Overdominant model | G/T | 1.1 | (0.85–1.42) | 0.48 | 0.94 | (0.71–1.26) | 0.69 | |
| Log - additive model | – | 1.07 | (0.87–1.32) | 0.53 | 1.05 | (0.83–1.32) | 0.7 | |
| rs3824999 | Allele model | C/A | 0.94 | (0.80–1.10) | 0.432 | 1.14 | (0.95–1.35) | 0.151 |
| Overdominant model | C/A | 0.81 | (0.64–1.04) | 0.1 | 0.98 | (0.75–1.28) | 0.9 | |
| Log - additive model | – | 0.92 | (0.77–1.10) | 0.37 | 1.13 | (0.93–1.37) | 0.21 | |
| rs3802842 | Allele model | C/A | 1.02 | (0.88–1.20) | 0.774 | 1.19 | (1.00–1.40) | 0.541 |
| Overdominant model | C/A | 0.99 | (0.77–1.26) | 0.91 | 1.09 | (0.84–1.42) | 0.52 | |
| Log - additive model | – | 0.96 | (0.81–1.14) | 0.68 | 1.14 | (0.94–1.37) | 0.18 | |
*p < 0.05, statistical significance