Gene-age interaction study of breast cancer prognosis based on epigenomic data
10.3760/cma.j.cn112338-20240201-00057
- VernacularTitle:基于表观基因组数据的乳腺癌预后基因-年龄交互作用研究
- Author:
Tianlin ZHOU
1
;
Maojie XUE
;
Zhixiang DAI
;
Ruyang ZHANG
;
Feng CHEN
Author Information
1. 南京医科大学公共卫生学院生物统计学系，南京 211166
- Keywords:
Breast cancer;
DNA methylation;
Age;
Interaction
- From:
Chinese Journal of Epidemiology
2024;45(7):1007-1013
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Exploring gene-age interactions associated with breast cancer prognosis based on epigenomic data.Methods:Differential expression analysis of DNA methylation was conducted using multiple independent epigenomic datasets of breast cancer from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The false discovery rate (FDR) method was used for multiple corrections, retaining differentially methylated sites with q-FDR≤0.05. A three-stage analytic strategy was implemented, using a multivariable Cox proportional hazards regression model to examine gene-age interactions. In the discovery phase, signals with q-FDR ≤ 0.05 were screened out using TCGA-BRCA database. In validation phaseⅠ, the interaction was validated using GSE72245 data, with criteria of P≤0.05 and consistent effect direction. In validation phaseⅡ, the signals were further validated using GSE37754 and GSE75067 data. A prognostic prediction model was constructed by incorporating clinical indicators and interaction signals. Results:The three-stage analytic strategy identified a methylation site (cg16126280 EBF1), which interacted with age to jointly affect the overall survival time of patients (interaction HR= 1.001 1,95% CI:1.000 7-1.001 5, P<0.001). Stratified analysis by age showed that the effect of hypermethylation of cg16126280 EBF1 was completely opposite in younger patients ( HR=0.550 5, 95% CI: 0.383 8-0.789 6, P=0.001) and older patients ( HR=2.166 5, 95% CI: 1.285 2-3.652 2, P=0.004). Conclusions:The DNA methylation site cg16126280 EBF1 exhibits an interaction with age, jointly influencing the prognosis of breast cancer in a complex association pattern. This finding contributes new population-based evidence for the development of age-specific targeted drugs.
