| Literature DB >> 29983348 |
Dongying Gu1, Rui Zheng2, Junyi Xin2, Shuwei Li2, Haiyan Chu2, Weida Gong3, Fulin Qiang4, Zhengdong Zhang2, Meilin Wang5, Mulong Du6, Jinfei Chen7.
Abstract
BACKGROUNDS: Genome-wide association studies (GWASs) have identified several gastric cancer (GC) susceptibility loci in Asians, but their effects on disease outcome are still unknown. This study aimed to investigate whether these GWAS-identified genetic variants could serve as robust prognostic biomarkers for GC.Entities:
Keywords: GWAS; Gastric cancer; Genetic variants; Survival
Mesh:
Substances:
Year: 2018 PMID: 29983348 PMCID: PMC6085567 DOI: 10.1016/j.ebiom.2018.06.028
Source DB: PubMed Journal: EBioMedicine ISSN: 2352-3964 Impact factor: 8.143
Fig. 1The flow chart for association analysis of the GWAS-identified SNPs and gastric cancer survival.
The association between rs2274223 in PLCE1 and gastric cancer patients' survival from three independent cohorts.
| Variation | Stages | Cohorts | AA/AG/GG | Genetic models | log-rank | HR (95% CI) | |||
|---|---|---|---|---|---|---|---|---|---|
| Patients | Deaths | MST (months) | |||||||
| Training set | Yixing | 509/361/68 | 256/150/31 | 52.9/96.4/68.5 | Additive model | 0.045 | 0.84 (0.72–0.98) | 0.026 | |
| Dominant model | 0.014 | 0.78 (0.65–0.95) | 0.011 | ||||||
| 0.036 | |||||||||
| Validation set1 | Nantong | 262/178/29 | 179/111/17 | 39.6/44.5/42.3 | Additive model | 0.359 | 0.80 (0.65–0.98) | 0.028 | |
| Dominant model | 0.166 | 0.78 (0.62–0.99) | 0.045 | ||||||
| 0.156 | |||||||||
| Validation set2 | Nanjing | 578/363/80 | 234/140/29 | 50.2 | Additive model | 0.493 | 0.91 (0.77–1.06) | 0.224 | |
| Dominant model | 0.263 | 0.86 (0.70–1.05) | 0.140 | ||||||
| 0.236 | |||||||||
| Combined sets | 1349/902/177 | 669/401/77 | 53.5/85.2/79.1 | Additive model | 0.014 | 0.86 (0.78–0.95) | 0.002 | ||
| Dominant model | 0.004 | 0.82 (0.73–0.93) | 0.001 | ||||||
| 0.005 | |||||||||
Mean survival time was calculated when Median survival time (MST) could not be performed.
Adjusted for age, sex, tumor size, histological type and TNM stage.
The evaluation of PLCE1 rs2274223 SNP as an independent factor for gastric cancer survival by stepwise Cox regression analysis.
| Variables | SE | HR (95% CI) | ||
|---|---|---|---|---|
| Age | ||||
| >60 | 0.172 | 0.061 | 1.18 (1.05–1.34) | 0.005 |
| Tumor size | ||||
| >5 | 0.331 | 0.064 | 1.39 (1.23–1.58) | <0.001 |
| Histological type | ||||
| Intestinal | −0.222 | 0.069 | 0.80 (0.70–0.92) | 0.001 |
| TNM stage | ||||
| I-II | 0.947 | 0.074 | 2.58 (2.23–2.98) | <0.001 |
| AG/GG | −0.196 | 0.061 | 0.82 (0.73–0.93) | 0.001 |
Fig. 2Distribution of onset age and the stratified effect of rs2274223 on gastric cancer survival. (a) shows the distribution of gastric cancer onset age in the combined cohorts; the early-onset age was defined as an age <45 years; (b) represents the association between rs2274223 and gastric cancer survival stratified by onset age; the hazard ratios were calculated by the Cox regression analysis with an adjustment for sex, tumor size, histological type and TNM stage in the dominant model.
Fig. 3Gastric cancer survival nomogram and corresponding calibration curve. (a) The nomogram allows the user to obtain the probability of three- and five-year overall survival corresponding to a patient's combination of covariates: locate patient's features on each axis, and compare to the “Point” axis to determine how many points are attributed to each feature; and then, locate the sum of the points for all variables on the “Total Points” line to determine the individual probability of gastric cancer on the “3-Year Survival” or “5-Year Survival” line. (b) The calibration curve of the nomogram for predicting gastric cancer overall survival. Actual overall survival is plotted on the y-axis, and predicted is on the x-axis.